{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一切行动基于spark集群启动之后。\n",
    "注意，一次只能启动一个spark集群，重复运行下面代码会报错。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
      "24/11/27 17:58:19 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.5.3\n"
     ]
    }
   ],
   "source": [
    "import random, sys, os, json, math, time\n",
    "\n",
    "import findspark\n",
    "\n",
    "#指定spark_home为刚才的解压路径,指定python路径\n",
    "spark_home = \"/mnt/user/linzhixin/mapreduce/spark\"\n",
    "python_path = \"/mnt/user/linzhixin/mapreduce/.venv/bin/python\"\n",
    "findspark.init(spark_home,python_path)\n",
    "\n",
    "import pyspark \n",
    "from pyspark import SparkContext, SparkConf\n",
    "conf = SparkConf().setAppName(\"rdd_tutorial\").setMaster(\"local[16]\")\n",
    "sc = SparkContext(conf=conf)\n",
    "\n",
    "print(pyspark.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载文件/数据结构\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['书名：三体全集(精校注释版)',\n",
       " '',\n",
       " '--------------------------------------------------------------------------------------------------------------------------------------',\n",
       " '☆本文由看帮网网友分享，仅供预览，请于下载后24小时内删除，不得用于商业用途，否则后果自负！看帮网（https://www.kanbang.cc）-精品电子书☆',\n",
       " '--------------------------------------------------------------------------------------------------------------------------------------',\n",
       " '',\n",
       " '   ',\n",
       " '祖籍河南，长于山西，中国科普作家协会会员，山西省作家协会会员，高级工程师。',\n",
       " '自1999年处女作《鲸歌》问世以来，发表短篇科幻小说三十余篇，出版长篇科幻小说七部，并创下连续八年荣获中国科幻最高奖“银河奖”的纪录。其气势恢宏的“地球往事”系列第一部《三体》开创了《科幻世界》月刊连载原创作品之先河，一举成为2006年度最受关注、最畅销的科幻小说。2008年，“地球往事”系列第二部《黑暗森林》面世，再次成为当年最畅销的科幻小说。2010年，“地球往事”系列第三部《死神永生》出版，一举拿下该年度中国科幻“银河奖”特别奖、全球华语科幻“星云奖”最佳长篇奖。刘慈欣亦因此被评论界誉为“21世纪中国文坛最值得关注的作家”。',\n",
       " '刘慈欣的作品恢弘大气、想象绚丽，既注意极端空灵与厚重现实的结合，也讲求科学的内涵与人文的美感，具有浓郁的中国特色和鲜明的个人风格，为中国科幻确立了一个新高度。',\n",
       " '',\n",
       " '写在“基石”之前 ',\n",
       " '■姚海军',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '“基石”是个平实的词，不够“炫”，却能够准确传达我们对构建中的中国科幻繁华巨厦的情感与信心，因此，我们用它来作为这套原创丛书的名字。',\n",
       " '最近十年，是科幻创作飞速发展的十年。王晋康、刘慈欣、何宏伟、韩松等一大批科幻作家发表了大量深受读者喜爱、极具开拓与探索价值的科幻佳作。科幻文学的龙头期刊更是从一本传统的《科幻世界》，发展壮大成为涵盖各个读者层的系列刊物。与此同时，科幻文学的市场环境也有了改善，省会级城市的大型书店里终于有了属于科幻的领地。',\n",
       " '仍然有人经常问及中国科幻与美国科幻的差距，但现在的答案已与十年前不同。在很多作品上（它们不再是那种毫无文学技巧与色彩、想象力拘谨的幼稚故事），这种比较已经变成了人家的牛排之于我们的土豆牛肉。差距是明显的——更准确地说，应该是“差别”——却已经无法再为它们排个名次。口味问题有了实际意义，这正是我们的科幻走向成熟的标志。',\n",
       " '与美国科幻的差距，实际上是市场化程度的差距。美国科幻从期刊到图书到影视再到游戏和玩具，已经形成了一条完整的产业链，动力十足；而我们的图书出版却仍然处于这样一种局面：读者的阅读需求不能满足的同时，出版者却感叹于科幻书那区区几千册的销量。结果，我们基本上只有为热爱而创作的科幻作家，鲜有为版税而创作的科幻作家。这不是有责任心的出版人所乐于看到的现状。',\n",
       " '科幻世界作为我国最有影响力的专业科幻出版机构，一直致力于对中国科幻的全方位推动。科幻图书出版是其中的重点之一。中国科幻需要长远眼光，需要一种务实精神，需要引入更市场化的手段，因而我们着眼于远景，而着手之处则在于一块块“基石”。',\n",
       " '需要特别说明的是，对于基石，我们并没有什么限定。因为，要建一座大厦需要各种各样的石料。',\n",
       " '对于那样一座大厦，我们满怀期待。',\n",
       " '',\n",
       " ' ',\n",
       " '',\n",
       " ' ',\n",
       " '',\n",
       " ' ',\n",
       " '',\n",
       " ' ',\n",
       " '',\n",
       " '',\n",
       " '地球往事·三体 ',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '文化大革命如火如荼进行的同时，军方探寻外星文明的绝秘计划“红岸工程”取得了突破性进展。但在按下发射键的那一刻，历经劫难的叶文洁没有意识到，她彻底改变了人类的命运。',\n",
       " '地球文明向宇宙发出的第一声啼鸣，以太阳为中心，以光速向宇宙深处飞驰……',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '四光年外，“三体文明”正苦苦挣扎——三颗无规则运行的太阳主导下的百余次毁灭与重生逼迫他们逃离母星。而恰在此时，他们接收到了地球发来的信息。',\n",
       " '在运用超技术锁死地球人的基础科学之后，三体人庞大的宇宙舰队开始向地球进发……人类的末日悄然来临。',\n",
       " '',\n",
       " ' ',\n",
       " '目\\u3000录 ',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '            前 言 ',\n",
       " '            1.\\u3000疯狂年代 ',\n",
       " '            2.\\u3000寂静的春天 ',\n",
       " '            3.\\u3000红岸之一 ',\n",
       " '            4.\\u3000三十八年后·科学边界 ',\n",
       " '            5.\\u3000台球 ',\n",
       " '            6.\\u3000射手和农场主 ',\n",
       " '            7.\\u3000三体、周文王、长夜 ',\n",
       " '            8.\\u3000叶文洁 ',\n",
       " '            9.\\u3000宇宙闪烁 ',\n",
       " '            10.\\u3000大史 ',\n",
       " '            11.\\u3000三体·墨子·烈焰 ',\n",
       " '            12.\\u3000红岸之二 ',\n",
       " '            13.\\u3000红岸之三 ',\n",
       " '            14.\\u3000红岸之四 ',\n",
       " '            15.\\u3000三体、哥白尼、宇宙橄榄球、三日凌空 ',\n",
       " '            16.\\u3000三体问题 ',\n",
       " '            17.\\u3000三体、牛顿、冯·诺依曼、秦始皇、三日连珠 ',\n",
       " '            18.\\u3000聚会 ',\n",
       " '            19.\\u3000三体、爱因斯坦、单摆、大撕裂 ',\n",
       " '            20.\\u3000三体、远征 ',\n",
       " '            21.\\u3000地球叛军 ',\n",
       " '            22.\\u3000红岸之五 ',\n",
       " '            23.\\u3000红岸之六 ',\n",
       " '            24.\\u3000叛乱 ',\n",
       " '            25.\\u3000雷志成、杨卫宁之死 ',\n",
       " '            26.\\u3000无人忏悔 ',\n",
       " '            27.\\u3000伊文斯 ',\n",
       " '            28.\\u3000第二红岸基地 ',\n",
       " '            29.\\u3000地球三体运动 ',\n",
       " '            30.\\u3000两个质子 ',\n",
       " '            31.\\u3000古筝行动 ',\n",
       " '            32.\\u3000监听员 ',\n",
       " '            33.\\u3000智子 ',\n",
       " '            34.\\u3000虫子 ',\n",
       " '            35.\\u3000尾声·遗址 ',\n",
       " '            后\\u3000记 ',\n",
       " '',\n",
       " '前\\u3000言 ',\n",
       " '《三体》终于能与科幻朋友们见面了，用连载的方式事先谁都没有想到，也是无奈之举。之前就题材问题与编辑们仔细商讨过，感觉没有什么问题，但没想到今年是文革三十周年这事儿，单行本一时出不了，也只能这样了。',\n",
       " '其实这本书不是文革题材的，文革内容在其中只占不到十分之一，但却是一个漂荡在故事中挥之不去的精神幽灵。',\n",
       " '本书虽不是《球状闪电》的续集，但可以看做那个故事所发生的世界在其后的延续，那个物理学家在故事中出现但已不重要，其他的人则永远消失了，林云真的死了，虽然我有时在想，如果她活下来，最后是不是这个主人公的样子？',\n",
       " '这是一个暂名为《地球往事》的系列的第一部，可以看做一个更长的故事的开始。',\n",
       " '这是一个关于背叛的故事，也是一个生存与死亡的故事，有时候，比起生存还是死亡来，忠诚与背叛可能更是一个问题。',\n",
       " '疯狂与偏执，最终将在人类文明的内部异化出怎样的力量？冷酷的星空将如何拷问心中道德？',\n",
       " '作者试图讲述一部在光年尺度上重新演绎的中国现代史，讲述一个文明二百次毁灭与重生的传奇。',\n",
       " '朋友们将会看到，连载的这第一期，几乎不是科幻，但这本书并不是这一期显示出来的这个样子，它不是现实科幻，比《球状闪电》更空灵，希望您能耐心地看下去，后面的故事变化会很大。',\n",
       " '在以后的一段时光中，读者朋友们将走过我在过去的一年中走过的精神历程，坦率地说，我不知道你们将在这条黑暗诡异的迷途上看到什么，我很不安。但科幻写到今天，能够与大家同行这么长一段时间，也是缘分。',\n",
       " '',\n",
       " '1.\\u3000疯狂年代 ',\n",
       " '中国，1967年。',\n",
       " '“红色联合”对“四·二八兵团”总部大楼的攻击已持续了两天，他们的旗帜在大楼周围躁动地飘扬着，仿佛渴望干柴的火种。',\n",
       " '“红色联合”的指挥官心急如焚，他并不惧怕大楼的守卫者，那二百多名“四·二八”战士，与诞生于1966年初、经历过大检阅和大串联的“红色联合”相比要稚嫩许多。他怕的是大楼中那十几个大铁炉子，里面塞满了烈性炸药，用电雷管串联起来，他看不到它们，但能感觉到它们磁石般的存在，开关一合，玉石俱焚，而“四·二八”的那些小红卫兵们是有这个精神力量的。比起已经在风雨中成熟了许多的第一代红卫兵，新生的造反派们像火炭上的狼群，除了疯狂还是疯狂。',\n",
       " '大楼顶上出现了一个娇小的身影，那个美丽的女孩子挥动着一面“四·二八”的大旗，她的出现立刻招来了一阵杂乱的枪声，射击的武器五花八门，有陈旧的美式卡宾枪、捷克式机枪和三八大盖，也有崭新的制式步枪和冲锋枪——后者是在“八月社论”发表之后从军队中偷抢来的  ——连同那些梭镖和大刀等冷兵器，构成了一部浓缩的近现代史……“四·二八”的人在前面多次玩过这个游戏，在楼顶上站出来的人，除了挥舞旗帜外，有时还用喇叭筒喊口号或向下撒传单，每次他们都能在弹雨中全身而退，为自己挣到崇高的荣誉。这次出来的女孩儿显然也相信自己还有那样的幸运。她挥舞着战旗，挥动着自己燃烧的青春，敌人将在这火焰中化为灰烬，理想世界明天就会在她那沸腾的热血中诞生……她陶醉在这鲜红灿烂的梦幻中，直到被一颗步枪子弹洞穿了胸膛，十五岁少女的胸膛是那么柔嫩，那颗子弹穿过后基本上没有减速，在她身后的空中发出一声啾鸣。年轻的红卫兵同她的旗帜一起从楼顶落下，她那轻盈的身体落得甚至比旗帜还慢，仿佛小鸟眷恋着天空。',\n",
       " '红色联合的战士们欢呼起来，几个人冲到楼下，掀开四·二八的旗帜，抬起下面纤小的遗体，作为一个战利品炫耀地举了一段，然后将她高高地扔向大院的铁门，铁门上带尖的金属栅条大部分在武斗初期就被抽走当梭镖了，剩下的两条正好挂住了她，那一瞬间，生命似乎又回到了那个柔软的躯体。红色联合的红卫兵们退后一段距离，将那个挂在高处的躯体当靶子练习射击，密集的子弹对她来说已柔和如雨，不再带来任何感觉，她那春藤般的手臂不时轻挥一下，仿佛拂去落在身上的雨滴，直到那颗年轻的头颅被打掉了一半，仅剩的一只美丽的眼睛仍然凝视着一九六七年的蓝天，目光中没有痛苦，只有凝固的激情和渴望。其实，比起另外一些人来，她还是幸运的，至少是在为理想献身的壮丽激情中死去。',\n",
       " '※※※',\n",
       " '这样的热点遍布整座城市，像无数并行运算的CPU，将“文化大革命”联为一个整体。疯狂如同无形的洪水，将城市淹没其中，并渗透到每一个细微的角落和缝隙。',\n",
       " '在城市边缘的那所著名大学的操场上，一场几千人参加的批斗会已经进行了近两个小时。在这个派别林立的年代，任何一处都有错综复杂的对立派别在格斗。在校园中，红卫兵、文革工作组、工宣队和军宣队，相互之间都在爆发尖锐的冲突，而每种派别的内部又时时分化出新的对立派系，捍卫着各自不同的背景和纲领，爆发更为残酷的较量。但这次被批斗的反动学术权威，却是任何一方均无异议的斗争目标，他们也只能同时承受来自各方的残酷打击。',\n",
       " '与其他牛鬼蛇神相比，反动学术权威有他们的特点：当打击最初到来时，他们的表现往往是高傲而顽固的，这也是他们伤亡率最高的阶段；在首都，四十天的时间里就有一千七百多名批斗对象被活活打死，更多的人则选择了用自杀的方式来维护自己的尊严。老舍、吴晗、葛伯赞、傅雷、赵九章、以群、闻捷、海默等，都自己结束了他们那曾经让人肃然起敬的生命。',\n",
       " '从这一阶段幸存下来的人，在持续的残酷打击下渐渐麻木，这是一种自我保护的精神外壳，使他们避免最后的崩溃。他们在批斗会上常常进入半睡眠状态，只有一声恫吓才能使其惊醒过来，机械地重复那已说过无数遍的认罪词；然后，他们中的一部分人便进入了第三阶段，旷日持久的批判将鲜明的政治图像如水银般注入了他们的意识，将他们那由知识和理性构筑的思想大厦彻底摧毁，他们真的相信自己有罪，真的看到了自己对伟大事业构成的损害，并为此痛哭流涕，他们的忏悔往往比那些非知识分子的牛鬼蛇神要深刻得多，也真诚得多。而对于红卫兵来说，进入后两个阶段的批判对象是最乏味的，只有处于第一阶段的牛鬼蛇神才能对他们那早已过度兴奋的神经产生有效的刺激，如同斗牛士手上的红布，但这样的对象越来越少了，在这所大学中可能只剩下一个，他由于自己的珍稀而被留到批判大会最后出场。',\n",
       " '叶哲泰从文革开始一直活到了现在，并且一直处于第一阶段，他不认罪，不自杀，也不麻木。当这位物理学教授走上批判台时，他那神情分明在说：让我背负的十字架更沉重一些吧！红卫兵们让他负担的东西确实很重，但不是十字架。别的批判对象戴的高帽子都是用竹条扎的框架，而他戴的这顶却是用一指粗的钢筋焊成的，还有他挂在胸前的那块牌子，也不是别人挂的木板，而是从实验室的一个烤箱上拆下的铁门，上面用黑色醒目地写着他的名字，并沿对角线画上了一个红色的大叉。',\n",
       " '押送叶哲泰上台的红卫兵比别的批判对象多了一倍，有六人，两男四女。两个男青年步伐稳健有力，一副成熟的青年布尔什维克形象，他们都是物理系理论物理专业大四年级的，叶哲泰曾是他们的老师；那四名女孩子要年轻得多，都是大学附中的初二学生，这些穿着军装扎着武装带的小战士挟带着逼人的青春活力，像四团绿色的火焰包围着叶哲泰。叶哲泰的出现使下面的人群兴奋起来，刚才已有些乏力的口号声又像新一轮海潮般重新高昂起来，淹没了一切。',\n",
       " '耐心地等口号声平息下去后，台上两名男红卫兵中的一人转向批判对象：“叶哲泰，你精通各种力学，应该看到自己正在抗拒的这股伟大的合力是多么强大，顽固下去是死路一条！今天继续上次大会的议程，废话就不多说了。老实回答下面的问题：在六二至六五届的基础课中，你是不是擅自加入了大量的相对论内容？！”',\n",
       " '“相对论已经成为物理学的古典理论，基础课怎么能不涉及它呢？”叶哲泰回答说。',\n",
       " '“你胡说！”旁边的一名女红卫兵厉声说，“爱因斯坦是反动的学术权威，他有奶便是娘，跑去为美帝国主义造原子弹！要建立起革命的科学，就要打倒以相对论为代表的资产阶级理论黑旗！”',\n",
       " '叶哲泰沉默着，他在忍受着头上铁高帽和胸前铁板带来的痛苦，不值得回应的问题就沉默了。在他身后，他的学生也微微皱了一下眉头。说话的女孩儿是这四个中学红卫兵中天资最聪颖的一个，并且显然有备而来，刚才上台前还看到她在背批判稿，但要对付叶哲泰，仅凭她那几句口号是不行的。他们决定亮出今天为老师准备的新武器，其中的一人对台下挥了一下手。',\n",
       " '叶哲泰的妻子，同系的物理学教授绍琳从台下的前排站起来，走上台。她身穿一件很不合体的草绿色衣服，显然想与红卫兵的色彩拉近距离，但熟悉绍琳的人联想到以前常穿精致旗袍讲课的她，总觉得别扭。',\n",
       " '“叶哲泰！”绍琳指着丈夫喝道，她显然不习惯这种场合，尽量拔高自己的声音，却连其中的颤抖也放大了，“你没有想到我会站出来揭发你，批判你吧！？是的，我以前受你欺骗，你用自己那反动世界观科学观蒙蔽了我！现在我醒悟了，在革命小将的帮助下，我要站到革命的一边，人民的一边！”她转向台下，“同志们、革命小将们、革命的教职员工们，我们应该认清爱因斯坦相对论的反动本质，这种本质，广义相对论体现得最清楚：它提出的静态宇宙模型  ，否定了物质的运动本性，是反辩证法的！它认为宇宙有限，更是彻头彻尾的反动唯心主义……”',\n",
       " '听着妻子滔滔不绝的演讲，叶哲泰苦笑了一下。琳，我蒙蔽了你？其实你在我心中倒一直是个谜。一次，我对你父亲称赞你那过人的天资——他很幸运，去得早，躲过了这场灾难——老人家摇摇头，说我女儿不可能在学术上有什么建树；接着，他说出了对我后半生很重要的一句话：琳琳太聪明了，可是搞基础理论，不笨不行啊。',\n",
       " '以后的许多年里，我不断悟出这话的深意。琳，你真的太聪明了，早在几年前，你就嗅出了知识界的政治风向，做出了一些超前的举动，比如你在教学中，把大部分物理定律和参数都改了名字，欧姆定律改叫电阻定律，麦克斯韦方程改名成电磁方程，普朗克常数叫成了量子常数……你对学生们解释说：所有的科学成果都是广大劳动人民智慧的结晶，那些资产阶级学术权威不过是窃取了这些智慧。但即使这样，你仍然没有被“革命主流”所接纳，看看现在的你，衣袖上没有“革命教职员工”都戴着的红袖章；你两手空空地上来，连一本语录都没资格拿……谁让你出生在旧中国那样一个显赫的家庭，你父母又都是那么著名的学者。',\n",
       " '说起爱因斯坦，你比我有更多的东西需要交代。1922年冬天，爱因斯坦到上海访问，你父亲因德语很好被安排为接待陪同者之一。你多次告诉我，父亲是在爱因斯坦的亲自教诲下走上物理学之路的，而你选择物理专业又是受了父亲的影响，所以爱翁也可以看作你的间接导师，你为此感到无比的自豪和幸福。',\n",
       " '后来我知道，父亲对你讲了善意的谎言，他与爱因斯坦只有过一次短得不能再短的交流。那是1922年11月13日上午，他陪爱因斯坦到南京路散步，同行的好像还有上海大学校长于右任、《大公报》经理曹谷冰等人，经过一个路基维修点，爱因斯坦在一名砸石子的小工身旁停下，默默看着这个在寒风中衣衫破烂、手脸污黑的男孩子，问你父亲：他一天挣多少钱？问过小工后，你父亲回答：五分。这就是他与改变世界的科学大师唯一的一次交流，没有物理学，没有相对论，只有冰冷的现实。据你父亲说，爱因斯坦听到他的回答后又默默地站在那里好一会儿，看着小工麻木的劳作，手里的烟斗都灭了也没有吸一口。你父亲在回忆这件事后，对我发出这样的感叹：在中国，任何超脱飞扬的思想都会砰然坠地的，现实的引力太沉重了。',\n",
       " '“低下头！”一名男红卫兵大声命令。这也许是自己的学生对老师一丝残存的同情，被批斗者都要低头，但叶哲泰要这样，那顶沉重的铁高帽就会掉下去，以后只要他一直低着头，就没有理由再给他戴上。但叶哲泰仍昂着头，用瘦弱的脖颈支撑着那束沉重的钢铁。',\n",
       " '“低头！你个反动顽固分子！！”旁边一名女红卫兵解下腰间的皮带朝叶哲泰挥去，黄铜带扣正打在他脑门上，在那里精确地留下了带扣的形状，但很快又被淤血模糊成黑紫的一团。他摇晃了一下，又站稳了。',\n",
       " '一名男红卫兵质问叶哲泰：“在量子力学  的教学中，你也散布过大量的反动言论！”说完对绍琳点点头，示意她继续。',\n",
       " '绍琳迫不及待地要继续下去了，她必须不停地说下去，以维持自己摇摇欲坠的精神免于彻底垮掉。“叶哲泰，这一点你无法抵赖！你多次向学生散布反动的哥本哈根解释  ！”',\n",
       " '“这毕竟是目前公认的最符合实验结果的解释。”叶哲泰说，在受到如此重击后，他的口气还如此从容，这让绍琳很吃惊，也很恐惧。',\n",
       " '“这个解释认为，是外部的观察导致了量子波函数的坍缩，这是反动唯心论的另一种表现形式，而且是一种最猖狂的表现！”',\n",
       " '“是哲学指引实验还是实验指引哲学？”叶哲泰问道，他这突然的反击令批判者们一时不知所措。',\n",
       " '“当然是正确的马克思主义哲学指引科学实验！”一名男红卫兵说。',\n",
       " '“这等于说正确的哲学是从天上掉下来的。反对实践出真知，恰恰是违背马克思主义对自然界的认知原则的。”',\n",
       " '绍琳和两名大学红卫兵无言以对，与中学和社会上的红卫兵不同，他们不可能一点儿道理也不讲。但来自附中的四位小将自有她们“无坚不摧”的革命方式，刚才动手的那个女孩儿又狠抽了叶哲泰一皮带，另外三个女孩子也都分别抡起皮带抽了一下，当同伴革命时，她们必须表现得更革命，至少要同样革命。两名男红卫兵没有过问，他们要是现在管这事，也有不革命的嫌疑。',\n",
       " '“你还在教学中散布宇宙大爆炸理论，这是所有科学理论中最反动的一个！”一名男红卫兵试图转移话题。',\n",
       " '“也许以后这个理论会被推翻，但本世纪的两大宇宙学发现：哈勃红移  和3K宇宙背景辐射  ，使大爆炸学说成为目前为止最可信的宇宙起源理论。”',\n",
       " '“胡说！”绍琳大叫起来，又接着滔滔不绝地讲起了宇宙大爆炸，自然不忘深刻地剖析其反动本质。但这理论的超级新奇吸引了四个小女孩儿中最聪明的那一个，她不由自主地问道：',\n",
       " '“连时间都是从那个奇点  开始的！？那奇点以前有什么？”',\n",
       " '“什么都没有。”叶哲泰说，像回答任何一个小女孩儿的问题那样，他转头慈祥地看着她，铁高帽和已受的重伤，使他这动作很艰难。',\n",
       " '“什么……都没有？！反动！反动透顶！！”那女孩儿惊恐万状地大叫起来，她不知所措地转向绍琳寻求帮助，立刻得到了回应。',\n",
       " '“这给上帝的存在留下了位置。”绍琳对女孩儿点点头提示说。',\n",
       " '小红卫兵那茫然的思路立刻找到了立脚点，她举起紧握皮带的手指着叶哲泰，“你，是想说有上帝？！”',\n",
       " '“我不知道。”',\n",
       " '“你说什么！”',\n",
       " '“我是说不知道，如果上帝是指宇宙之外的超意识的话，我不知道它是不是存在；正反两方面，科学都没给出确实的证据。”其实，在这噩梦般的时刻，叶哲泰已倾向于相信它不存在了。',\n",
       " '这句大逆不道的话在整个会场引起了骚动，在台上一名红卫兵的带领下，又爆发了一波波的口号声。',\n",
       " '“打倒反动学术权威叶哲泰！！”',\n",
       " '“打倒一切反动学术权威！！”',\n",
       " '“打倒一切反动学说！！”',\n",
       " '……',\n",
       " '“上帝是不存在的，一切宗教，都是统治阶级编造出来的麻痹人民的精神工具！”口号平息后，那个小女孩儿大声说。',\n",
       " '“这种看法是片面的。”叶哲泰平静地说。',\n",
       " '恼羞成怒的小红卫兵立刻做出了判断，对于眼前这个危险的敌人，一切语言都无意义了。她抡起皮带冲上去，她的三个小同志立刻跟上，叶哲泰的个子很高，这四个十四岁的女孩儿只能朝上抡皮带才能打到他那不肯低下的头，在开始的几下打击后，他头上能起一定保护作用的铁高帽被打掉了，接下来带铜扣的宽皮带如雨点般打在他的头上和身上——他终于倒下了，这鼓舞了小红卫兵们，她们更加投入地继续着这“崇高”的战斗，她们在为信念而战，为理想而战，她们为历史给予自己的光辉使命所陶醉，为自己的英勇而自豪……',\n",
       " '“最高指示：要文斗不要武斗！”叶哲泰的两名学生终于下定了决心，喊出了这句话，两人同时冲过去，拉开了已处于半疯狂状态的四个小女孩儿。',\n",
       " '但已经晚了，物理学家静静地躺在地上，半睁的双眼看着从他的头颅上流出的血迹，疯狂的会场瞬间陷入了一片死寂，那条血迹是唯一在动的东西，它像一条红蛇缓慢地蜿蜒爬行着，到达台沿后一滴滴地滴在下面一个空箱子上，发出有节奏的“嗒嗒”声，像渐行渐远的脚步。',\n",
       " '一阵怪笑声打破了寂静，这声音是精神已彻底崩溃的绍琳发出的，听起来十分恐怖。人们开始离去，最后发展成一场大溃逃，每个人都想尽快逃离这个地方。会场很快空了下来，只剩下一个姑娘站在台下。',\n",
       " '她是叶哲泰的女儿叶文洁。',\n",
       " '当那四个女孩儿施暴夺去父亲生命时，她曾想冲上台去，但身边的两名老校工死死抓住她，并在耳边低声告诉她别连自己的命也不要了，当时会场已经处于彻底的癫狂，她的出现只会引出更多的暴徒。她曾声嘶力竭地哭叫，但声音淹没在会场上疯狂的口号和助威声中，当一切寂静下来时，她自己也发不出任何声音了，只是凝视着台上父亲已没有生命的躯体，那没有哭出和喊出的东西在她的血液中弥漫、溶解，将伴她一生。人群散去后，她站在那里，身体和四肢仍保持着老校工抓着她时的姿态，一动不动，像石化了一般。过了好久，她才将悬空的手臂放下来，缓缓起身走上台，坐在父亲的遗体边，握起他的一只已凉下来的手，两眼失神地看着远方。当遗体要被抬走时，叶文洁从衣袋中拿出一样东西放到父亲的那只手中，那是父亲的烟斗。',\n",
       " '文洁默默地离开了已经空无一人一片狼藉的操场，走上回家的路。当她走到教工宿舍楼下时，听到了从二楼自家窗口传出的一阵阵痴笑声，这声音是那个她曾叫做妈妈的女人发出的。文洁默默地转身走去，任双脚将她带向别处。',\n",
       " '她最后发现自己来到了阮雯的家门前，在大学四年中，阮老师一直是她的班主任，也是她最亲密的朋友。在叶文洁读天体物理专业研究生的两年里，再到后来停课闹革命至今，阮老师一直是她除父亲外最亲近的人。阮雯曾留学剑桥，她的家曾对叶文洁充满了吸引力，那里有许多从欧洲带回来的精致的书籍、油画和唱片，一架钢琴；还有一排放在精致小木架上的欧式烟斗，父亲那只就是她送的，这些烟斗有地中海石楠根的，有土耳其海泡石的，每一个都仿佛浸透了曾将它们拿在手中和含在嘴里深思的那个男人的智慧，但阮雯从未提起过他。这个雅致温暖的小世界成为文洁逃避尘世风暴的港湾。但那是阮雯的家被抄之前的事，她在运动中受到的冲击和文洁父亲一样重，在批斗会上，红卫兵把高跟鞋挂到她脖子上，用口红在她的脸上划出许多道子，以展示她那腐朽的资产阶级生活方式。',\n",
       " '叶文洁推开阮雯的家门，发现抄家后混乱的房间变得整洁了，那几幅被撕的油画又贴糊好挂在墙上，歪倒的钢琴也端正地立在原位，虽然已被砸坏不能弹了，但还是擦得很干净，残存的几本精装书籍也被整齐地放回书架上……阮雯端坐在写字台前的那把转椅上，安详地闭着双眼。叶文洁站在她身边，摸摸她的额头、脸和手，都是冰凉的，其实文洁在进门后就注意到了写字台上倒放着的那个已空的安眠药瓶。她默默地站了一会儿，转身走去，悲伤已感觉不到了，她现在就像一台盖革计数仪，当置身于超量的辐射中时，反而不再有任何反应，没有声响，读数为零。但当她就要出门时，还是回过头来最后看了阮雯一眼，她发现阮老师很好地上了妆，她抹了口红，也穿上了高跟鞋。',\n",
       " '1967年8月《红旗》杂志发表“揪军内一小撮”的社论，使冲击军区、抢夺军队枪支弹药的事件愈演愈烈，全国范围的武斗也进入高潮。 ',\n",
       " '1917年，爱因斯坦根据狭义相对论提出的宇宙模型，主张宇宙是一个体积有限没有边界的静态弯曲封闭体。这个模型克服了牛顿理论和无限宇宙的矛盾，是第一个自洽统一的动力学宇宙模型，是现代宇宙学的开端。 ',\n",
       " '量子力学（Quantum Mechanics）是研究微观粒子的运动规律的物理学分支学科，它主要研究原子、分子、凝聚态物质以及原子核和基本粒子的结构、性质的基础理论，它与相对论一起构成了现代物理学的理论基础。量子力学包括光与物质的相互作用、原子结构、物质衍射等关键现象。它不仅是近代物理学的基础理论之一，而且在化学等有关学科和许多近代技术中也得到了广泛的应用。 ',\n",
       " '哥本哈根解释是建立在由德国数学家、物理学家马克思·波恩（Max Born）所提出的“波函数的概率表达”上，之后发展为著名的不确定原理，即震动中的微粒子——量子的类弦的决定论诠释。与经典物理不同的是，在量子物理中所有涉及的测量值都不可以精确的预测，但我们可以通过概率（偶然性）来预测。外部的观察将导致量子波函数的坍缩，即当我们用物理方式对其进行测量时（同时必然对其干扰），物质随机选择一个单一结果表现出来。如果我们把波函数比作是骰子的话（比如电子云），那么“波函数坍缩”就是骰子落地（比如打在屏幕上显示为一个点的电子），其具有随意性。 ',\n",
       " '哈勃红移：即光的多普勒效应。当用望远镜观察一个高速远离地球的天体时，它的光谱（通俗而言便是颜色）就要向红色方向移动，这就是红移。与红移相对的是蓝移，即观察一个高速靠近地球的天体时，其光谱要向蓝色方向移动。 ',\n",
       " '3K宇宙背景辐射：又称宇宙微波背景辐射，它是一种充满整个宇宙的各向同性的电磁辐射，它的频率属于微波范围。宇宙微波背景辐射产生于大爆炸后的三十万年：发生大爆炸时，宇宙的温度是极高的，之后它开始慢慢降温，刚到现在约150亿年后大约还残留着3K左右的热辐射。背景辐射波长远大于可见光，所以其对人类是不可见的。在科学观测中，天线系统接收到的消除不掉的背景噪音，实际上就是宇宙背景辐射。 ',\n",
       " '作为“宇宙学的奇点”，是宇宙产生之初，由爆炸而形成现在宇宙的那一点。在奇点处，所有定律以及可预见性都失效，奇点可以看成空间时间的边缘或边界。 ',\n",
       " '',\n",
       " '2.\\u3000寂静的春天 ',\n",
       " '两年以后，大兴安岭。',\n",
       " '“顺山倒咧——”',\n",
       " '随着这声嘹亮的号子，一棵如巴特农神庙的巨柱般高大的落叶松轰然倒下，叶文洁感到大地抖动了一下。她拿起斧头和短锯，开始去除巨大树身上的枝丫。每到这时，她总觉得自己是在为一个巨人整理遗体。她甚至常常有这样的想象：这巨人就是自己的父亲。两年前那个凄惨的夜晚，她在太平间为父亲整理遗容时的感觉就在这时重现。巨松上那绽开的树皮，似乎就是父亲躯体上累累的伤痕。',\n",
       " '内蒙古生产建设兵团的六个师四十一个团十多万人就分布在这辽阔的森林和草原之间。刚从城市来到这陌生的世界时，很多兵团知青都怀着一个浪漫的期望：当苏修帝国主义的坦克集群越过中蒙边境时，他们将飞快地武装起来，用自己的血肉构成共和国的第一道屏障。事实上，这也确实是兵团组建时的战略考虑之一。但他们渴望的战争就像草原天边那跑死马的远山，清晰可见，但到不了眼前，于是他们只有垦荒、放牧和砍伐。这些曾在“大串联”中燃烧青春的年轻人很快发现，与这广阔天地相比，内地最大的城市不过是个羊圈；在这寒冷无际的草原和森林间，燃烧是无意义的，一腔热血喷出来，比一堆牛粪凉得更快，还不如后者有使用价值。但燃烧是他们的命运，他们是燃烧的一代。于是，在他们的油锯和电锯下，大片的林海化为荒山秃岭；在他们的拖拉机和康拜因（联合收割机）下，大片的草原被犁成粮田，然后变成沙漠。',\n",
       " '叶文洁看到的砍伐只能用疯狂来形容，高大挺拔的兴安岭落叶松、四季常青的樟子松、亭亭玉立的白桦、耸入云天的山杨、西伯利亚冷杉，以及黑桦、柞树、山榆、水曲柳、钻天柳、蒙古栎，见什么伐什么，几百把油锯如同一群钢铁蝗虫，她的连队所过之处，只剩下一片树桩。',\n",
       " '整理好的落叶松就要被履带拖拉机拖走了，在树干另一头，叶文洁轻轻抚摸了一下那崭新的锯断面，她常常下意识地这么做，总觉得那是一处巨大的伤口，似乎能感到大树的剧痛。她突然看到，在不远处树桩的锯断面上，也有一只在轻轻抚摸的手，那手传达出的心灵的颤抖，与她产生了共振。那手虽然很白皙，但能够看出是属于男性的。叶文洁抬头，看到抚摸树桩的人是白沐霖，一个戴眼镜的瘦弱青年，他是兵团《大生产报》的记者，前天刚到连队来采访。叶文洁看过他写的文章，文笔很好，其中有一种与这个粗放环境很不协调的纤细和敏感，令她很难忘。',\n",
       " '“马钢，你过来。”白沐霖对不远处一个小伙子喊道，那人壮得像这棵刚被他伐倒的落叶松。他走过来，白记者问道：“你知道这棵树多大年纪了？”',\n",
       " '“数数呗。”马钢指指树桩上的年轮说。',\n",
       " '“我数了，三百三十多岁呢。你锯倒它用了多长时间？”',\n",
       " '“不到十分钟吧，告诉你，我是连里最快的油锯手，我到哪个班，流动红旗就跟我到那儿。”马钢看上去很兴奋，让白记者注意到的人都这样，能在《大生产报》的通讯报道上露一下脸也是很光荣的事。',\n",
       " '“三百多年，十几代人啊，它发芽时还是明朝呢，这漫长的岁月里，它经历过多少风雨，见过多少事。可你几分钟就把它锯倒了，你真没感觉到什么？”',\n",
       " '“你想让我感觉到什么呢？”马钢愣了一下，“不就一棵树嘛，这里最不缺的就是树，比它岁数长的老松多的是。”',\n",
       " '“忙你的去吧。”白沐霖摇摇头，坐在树桩子上轻轻叹息了一声。马钢也摇摇头，记者没有报道他的兴趣，令他很失望。“知识分子毛病就是多。”他说的时候还瞟了一眼不远处的叶文洁，他的话显然也包括了她。',\n",
       " '大树被拖走了，地面上的石块和树桩划开了树皮，使它巨大的身躯皮开肉绽。它原来所在的位置上，厚厚的落叶构成的腐殖层被压出了一条长沟，沟里很快渗出了水，陈年落叶使水呈暗红色，像血。',\n",
       " '※※※',\n",
       " '“小叶，过来歇歇吧。”白沐霖指指大树桩空着的另一边对叶文洁说。文洁确实累了，放下工具，走过来和记者背靠背地坐着。',\n",
       " '沉默了好一会儿，白沐霖突然说：“我看得出来你的感觉，在这里也就我们俩有这种感觉。”',\n",
       " '文洁仍然沉默着，白沐霖预料她不会回答。叶文洁平时沉默寡言，很少与人交流，有些刚来的人甚至误认为她是哑巴。',\n",
       " '白沐霖自顾自地说下去：“一年前打前站时我就到过这个林区，记得刚到时是晌午，接待我们的人说要吃鱼，我在那间小树皮屋里四下看看，就烧着一锅水，哪有鱼啊；水开后，见做饭的人拎着擀面杖出去，到屋前的那条小河中‘乒乓’几棒子，就打上几条大鱼来……多富饶的地方，可现在看看那条河，一条什么都没有的浑水沟。我真不知道，现在整个兵团的开发方针是搞生产还是搞破坏？”',\n",
       " '“你这种想法是从哪儿来呢？”叶文洁轻声问，并没有透露出她对这想法是赞同还是反对，但她能说话，已经让白沐霖很感激了。',\n",
       " '“我刚看了一本书，感触很深……你能读英文吧？”看到文洁点点头，白沐霖从包中掏出一本蓝色封面的书，在递给文洁时，他有意无意地四下看了看，“这本书是六二年出的，在西方影响很大。”',\n",
       " '    ',\n",
       " '《寂静的春天》封面 ',\n",
       " '文洁转身接过书，看到书名是《SILENT SPRING》（《寂静的春天》）  ，作者是Rachel Carson  。“哪儿来的？”她轻声问。',\n",
       " '“这本书引起了上级的重视，要搞内参，我负责翻译与森林有关的那部分。”',\n",
       " '文洁翻开书，很快被吸引住了，在短短的序章中，作者描述了一个在杀虫剂的毒害下正在死去的寂静的村庄，平实的语言背后显现着一颗忧虑的心。',\n",
       " '“我想给中央写信，反映建设兵团这种不负责任的行径。”白沐霖说。',\n",
       " '叶文洁从书上抬起头来，好半天才明白他意思，没说什么又低头看书。',\n",
       " '“你要想看就先拿着，不过最好别让其他人看见，这东西，你知道……”白沐霖说着，又四下看了看，起身离去。',\n",
       " '三十八年后，在叶文洁的最后时刻，她回忆起《寂静的春天》对自己一生的影响。在这之前，人类恶的一面已经在她年轻的心灵上刻下不可愈合的巨创，但这本书使她对人类之恶第一次进行了理性的思考。这本来应该是一本很普通的书，主题并不广阔，只是描述杀虫剂的滥用对环境造成的危害，但作者的视角对叶文洁产生了巨大的震撼：蕾切尔·卡逊所描写的人类行为——使用杀虫剂，在文洁看来只是一项正当和正常的、至少是中性的行为；而本书让她看到，从整个大自然的视角看，这个行为与“文化大革命”是没有区别的，对我们的世界产生的损害同样严重。那么，还有多少在自己看来是正常甚至正义的人类行为是邪恶的呢？',\n",
       " '再想下去，一个推论令她不寒而栗，陷入恐惧的深渊：也许，人类和邪恶的关系，就是大洋与漂浮于其上的冰山的关系，它们其实是同一种物质组成的巨大水体，冰山之所以被醒目地认出来，只是由于其形态不同而已，而它实质上只不过是这整个巨大水体中极小的一部分……人类真正的道德自觉是不可能的，就像他们不可能拔着自己的头发离开大地。要做到这一点，只有借助于人类之外的力量。',\n",
       " '这个想法最终决定了叶文洁的一生。',\n",
       " '四天后，叶文洁去还书。白沐霖住在连队唯一的一间招待房里，文洁推开门，见他疲惫地躺在床上，一身泥水和木屑，见到文洁，他赶紧起身。',\n",
       " '“今天干活儿了？”文洁问。',\n",
       " '“下连队这么长时间了，不能总是甩手到处转，劳动得参加，三结合嘛。哦，我们在雷达峰干，那里林木真密，地下的腐叶齐膝深，我真怕中了瘴气。”白沐霖说。',\n",
       " '“雷达峰？！”文洁听到这个名字很吃惊。',\n",
       " '“是啊，团里下的紧急任务，要围着它伐出一圈警戒带。”',\n",
       " '雷达峰是一个神秘的地方，那座陡峭的奇峰本没有名字，只是因为它的峰顶有一面巨大的抛物面天线才得此名。其实，稍有常识的人都知道那不是雷达天线，虽然它的方向每天都会变化，但从未连续转动过。那天线在风中发出低沉的嗡嗡声，很远都能听到。连队的人只知道那是一个军事基地，听当地人说，三年前建设那个基地时，曾动用巨大的人力，向峰顶架设了一条高压线，开辟了一条通向峰顶的公路，有大量的物资沿公路运上去。但基地建成后，竟把这条公路拆毁了，只留下一条勉强能通行的林间小路，常有直升机在峰顶起降。',\n",
       " '那座天线并不总是出现，风太大时它会被放倒，而当它立起来时，就会发生许多诡异的事情：林间的动物变得焦躁不安，林鸟被大群地惊起，人也会出现头晕恶心等许多不明症状。在雷达峰附近的人还特别容易掉头发，据当地人说，这也是天线出现后才有的事。',\n",
       " '雷达峰有许多神秘的传说：一次下大雪，那个天线立起来，这方圆几里的雪立刻就变成了雨！严寒中，雨水在树上冻成冰，每棵树都挂起了大冰挂子，森林成了水晶宫，其间不断地响着树枝被压断的“咔嚓”声和冰挂子坠地的“轰轰”声。有时，在天线立起时，晴空会出现雷电，夜间天空中能看到奇异的光晕……雷达峰警戒森严，建设兵团的连队驻扎后，连长第一件事就是让所有人注意不要擅自靠近雷达峰，否则基地的岗哨可以不经警告就开枪。上星期，连队里两个打猎的兵团战士追一只狍子，不知不觉追到了雷达峰下，立刻招来了来自半山腰上岗亭的急促射击，幸亏林子密，两人没伤着跑了回来，其中一个吓得尿了一裤子。第二天连里开会，每人挨了一个警告处分。可能正是因为这事，基地才决定在周围的森林中开伐一圈警戒带，而兵团的人力可以随他们调用，也可见其行政级别很高。',\n",
       " '白沐霖接过书，小心地放到枕头下面，同时从那里拿出了几页写得密密麻麻的稿纸，递给文洁，“这是那封信的草稿，你看看行吗？”',\n",
       " '“信？”',\n",
       " '“我跟你说过的，要给中央写信。”',\n",
       " '纸上的字迹很潦草，叶文洁很吃力地看完了。这封信立论严谨，内容丰富：从太行山因植被破坏，由历史上的富庶之山变成今天贫瘠的秃岭，到现代黄河泥沙含量的急剧增加，得出了内蒙古建设兵团的大垦荒将带来严重后果的结论。文洁这才注意到，他的文笔真的与《寂静的春天》很相似，平实精确而蕴涵诗意，令理科出身的她感到很舒适。',\n",
       " '“写得很好。”她由衷地赞叹道。',\n",
       " '白沐霖点点头，“那我寄出去了。”说着拿出了一本新稿纸要誊抄，但手抖得厉害，一个字都写不出来。第一次使油锯的人都是这样，手抖得可能连饭碗都端不住，更别说写字了。',\n",
       " '“我替你抄吧。”叶文洁说，接过白沐霖递来的笔抄了起来。',\n",
       " '“你字写得真好。”白沐霖看着稿纸上抄出的第一行字说，他给文洁倒了一杯水，手仍然抖得厉害，水洒出来不少，文洁忙把信纸移开些。',\n",
       " '“你是学物理的？”白沐霖问。',\n",
       " '“天体物理，现在没什么用处了。”文洁回答，没有抬头。',\n",
       " '“那就是研究恒星吧，怎么会没用处呢？现在大学都已复课，但研究生不再招了，你这样的高级人才窝到这种地方，唉……”',\n",
       " '文洁没有回答，只是埋头抄写，她不想告诉白沐霖，自己能进入建设兵团已经很幸运了。对于现实，她什么都不想说，也没什么可说的了。',\n",
       " '屋里安静下来，只有钢笔尖在纸上划动的沙沙声。文洁能闻到身边记者身上松木锯末的味道，自父亲惨死后，她第一次有一种温暖的感觉，第一次全身心松弛下来，暂时放松了对周围世界的戒心。',\n",
       " '一个多小时后，信抄完了，又按白沐霖说的地址和收信人写好了信封，文洁起身告辞，走到门口时，她回头说：“把你的外衣拿来，我帮你洗洗吧。”说完后，她对自己的这一举动很吃惊。',\n",
       " '“不，那哪行！”白沐霖连连摆手说，“你们建设兵团的女战士，白天干的都是男同志的活儿，快回去休息吧，明天六点就要上山呢。哦，文洁，我后天就要回师部，我会把你的情况向上级反映一下，也许能帮上忙呢。”',\n",
       " '“谢谢，不过我觉得这里很好，挺安静的。”文洁看着月光下大兴安岭朦胧的林海说。',\n",
       " '“你是不是在逃避什么？”',\n",
       " '“我走了。”叶文洁轻声说，转身离去。',\n",
       " '白沐霖看着她那纤细的身影在月光下消失，然后，他抬头遥望文洁刚才看过的林海，看到远方的雷达峰上，巨大的天线又缓缓立起，闪着金属的冷光。',\n",
       " '※※※',\n",
       " '三个星期后的一天中午，叶文洁被从伐木场紧急召回连部。一走进办公室，她就发现气氛不对，连长和指导员都在，还有一个表情冷峻的陌生人，他面前的办公桌上放着一个黑色的公文包，旁边两件东西显然是从公文包中拿出来的，那是一个信封和一本书，信封是拆开的，书就是那本她看过的《寂静的春天》。',\n",
       " '这个年代的人对自己的政治处境都有一种特殊的敏感，而这种敏感在叶文洁身上更强烈一些，她顿时感到周围的世界像一个口袋般收紧，一切都向她挤压过来。',\n",
       " '“叶文洁，这是师政治部来调查的张主任，”指导员指指陌生人说，“希望你配合，要讲实话。”',\n",
       " '“这封信是你写的吗？”张主任问，同时从信封中抽出信来。叶文洁伸手去拿，但张主任没给她，仍把信拿在自己手中，一页一页翻给她看，终于翻到了她想看的最后一页，落款上没有姓名，只写着“革命群众”四个字。',\n",
       " '“不，不是我写的。”文洁惊恐地摇摇头。',\n",
       " '“可这是你的笔迹。”',\n",
       " '“是，可我是帮别人抄的。”',\n",
       " '“帮谁？”平时在连队遇到什么事，叶文洁很少为自己申辩，所有的亏都默默地吃了，所有的委屈都默默地承受，更不用说牵连别人了。但这次不同，她很清楚这意味着什么。',\n",
       " '“是帮那位上星期到连队来采访的《大生产报》记者抄的，他叫……”',\n",
       " '“叶文洁！”张主任的眼睛像两个黑洞洞的枪口对着她，“我警告你，诬陷别人会使你的问题更加严重。我们已经从白沐霖同志那里调查清楚了，他只是受你之托把信带到呼和浩特发出去，并不知道信的内容。”',\n",
       " '“他……是这么说的？！”文洁眼前一黑。',\n",
       " '张主任没有回答她的话，而是拿起了那本书，“你写这封信，一定是受到了它的启发。”他把书对着连长和指导员展示了一下，“这本书叫《寂静的春天》，1962年在美国出版，在资本主义世界影响很大。”他接着从公文包中拿出了另一本书，封面是白皮黑字，“这是这本书的中译本，是有关部门以内参形式下发的，供批判用。现在，上级对这本书已经做出了明确的定性：这是一部反动的大毒草。该书从唯心史观出发，宣扬末世论  ，借环境问题之名，为资本主义世界最后的腐朽没落寻找托辞，其实质是十分反动的。”',\n",
       " '“可是……这本书也不是我的。”文洁无力地说。',\n",
       " '“白沐霖同志是上级指定的本书译者之一，他携带这本书是完全合法的，当然，他也有保管责任，不该让你趁他在劳动中不备时偷拿去看——现在，你从这本书中找到了向社会主义进攻的思想武器。”',\n",
       " '叶文洁沉默了，她知道自己已经掉到陷阱的底部，任何挣扎都是徒劳的。',\n",
       " '与后来人们熟知的一些历史记载相反，白沐霖当初并非有意陷害叶文洁，他写给中央的那封信也可能是出于真诚的责任心。那时怀着各种目的直接给中央写信的人很多，大多数信件石沉大海，也有少数人因此一夜之间飞黄腾达或面临灭顶之灾。当时的政治神经是极其错综复杂的，作为记者，白沐霖自以为了解这神经系统的走向和敏感之处，但他过分自信了，他这封信触动了他以前不知道的雷区。得知消息后，恐惧压倒了一切，他决定牺牲叶文洁，保护自己。',\n",
       " '半个世纪后，历史学家们一致认为，1969年的这一事件是以后人类历史的一个转折点。白沐霖无意之中成为一个标志性的关键历史人物，但他自己没有机会知道这点，历史学家们失望地记载了他平淡的余生。白沐霖在《大生产报》一直工作到1975年，那时内蒙古建设兵团撤销，他调到一个东北城市的科协工作至上世纪八十年代初，然后出国到加拿大，在渥太华一所华语学校任教师至1991年，患肺癌去世。余生中他没对任何人提起过叶文洁的事，是否感到过自责和忏悔也不得而知。',\n",
       " '“小叶啊，连里对你可是仁至义尽了。”连长喷出一口辣烈的莫合烟，看着地面说，“你出身和家庭背景都不好，可我们没把你当外人。针对你脱离群众、不积极要求进步的倾向，我和指导员都多次找你谈过，想帮助你。谁想到，你竟犯了这么严重的错误！”',\n",
       " '“我早就看出来，她对‘文化大革命’的抵触情绪是根深蒂固的。”指导员接着说。',\n",
       " '“下午派两个人，把她和这些罪证一起送到师部。”张主任面无表情地说。',\n",
       " '※※※',\n",
       " '同室的三名女犯相继被提走，监室里只剩叶文洁一个人了。墙角的那一小堆煤用完了也没人来加，炉子很快灭了，监室里冷了下来，叶文洁不得不将被子裹在身上。',\n",
       " '天黑前来了两个人，其中一名是年长些的女干部，随行的那人介绍说她是中级法院军管会的军代表  。',\n",
       " '“程丽华。”女干部自我介绍说，她四十多岁，身穿军大衣，戴着一副宽边眼镜，脸上线条柔和，看得出年轻时一定很漂亮，说话时面带微笑，让人感到平易近人。叶文洁清楚，这样级别的人来到监室见一个待审的犯人，很不寻常。她谨慎地对程丽华点点头，起身在狭窄的床铺上给她让出坐的地方。',\n",
       " '“这么冷，炉子呢？”程丽华不满地看了站在门口的看守所所长一眼，又转向文洁，“嗯，年轻，你比我想的还年轻。”说完坐在床上，离文洁很近，低头翻起公文包来，嘴里还像老大妈似的嘟囔着，“小叶你糊涂啊，年轻人都这样，书越读得多越糊涂了，你呀你呀……”她找到了要找的东西，把那一小打文件抱在胸前，抬头看着叶文洁，目光中充满了慈爱，“不过，年轻人嘛，谁没犯过错误？我就犯过，那时我在四野的文工团，苏联歌曲唱得好，一次政治学习会上，我说我们应该并入苏联，成为苏维埃社会主义联盟的一个新共和国，这样国际共产主义的力量就更强大了……幼稚啊，可谁没幼稚过呢？还是那句话，不要有思想负担，有错就认识就改，然后继续革命嘛。”',\n",
       " '程丽华的一席话拉近了叶文洁与她的距离，但叶文洁在灾难中学会了谨慎，她不敢贸然接受这份奢侈的善意。',\n",
       " '程丽华把那叠文件放到叶文洁面前的床面上，递给她一支笔，“来，先签了字，咱们再好好谈谈，解开你的思想疙瘩。”她的语气，仿佛在哄一个小孩儿吃奶。',\n",
       " '叶文洁默默地看着那份文件，一动不动，没有去接笔。',\n",
       " '程丽华宽容地笑笑，“你是可以相信我的，我以人格保证，这文件内容与你的案子无关，签字吧。”',\n",
       " '站在一边的那名随行者说：“叶文洁，程代表是想帮你的，她这几天为你的事可没少操心。”',\n",
       " '程丽华挥手制止他说下去。“能理解的，这孩子，唉，给吓坏了。现在一些人的政策水平实在太低，建设兵团的，还有你们法院的，方法简单，作风粗暴，像什么样子！好吧，小叶，来，看看文件，仔细看看吧。”',\n",
       " '叶文洁拿起文件，在监室昏黄的灯光下翻看着。程代表没骗她，这份材料确实与她的案子无关，是关于她那已死去的父亲的。其中记载了父亲与一些人交往情况和谈话内容，',\n",
       " '文件的提供者是叶文洁的妹妹叶文雪。作为一名最激进的红卫兵，叶文雪积极主动地揭发父亲，写过大量的检举材料，其中的一些直接导致了父亲的惨死。但这一份材料文洁一眼就看出不是妹妹写的，文雪揭发父亲的材料文笔激烈，读那一行行字就像听着一挂挂炸响的鞭炮，但这份材料写得很冷静、很老到，内容翔实精确，谁谁谁哪年哪月哪日在哪里见了谁谁谁又谈了什么，外行人看去像一本平淡的流水账，但其中暗藏的杀机，绝非叶文雪那套小孩子把戏所能相比的。',\n",
       " '材料的内容她看不太懂，但隐约感觉到与一个重大国防工程有关。作为物理学家的女儿，叶文洁猜出了那就是从1964年开始震惊世界的中国两弹工程。在这个年代，要搞倒一个位置很高的人，就要在其分管的各个领域得到他的黑材料，但两弹工程对阴谋家们来说是个棘手的领域，这个工程处于中央的重点保护之下，得以避开“文革”的风雨，他们很难插手进去。',\n",
       " '由于出身问题没通过政审，父亲并没有直接参加两弹研制，只是做了一些外围的理论工作，但要利用他，比利用两弹工程的那些核心人物更容易些。叶文洁不知道材料上那些内容是真是假，但可以肯定，上面的每一个标点符号都具有致命的政治杀伤力。除了最终的打击目标外，还会有无数人的命运要因这份材料坠入悲惨的深渊。材料的末尾是妹妹那大大的签名，而叶文洁是要作为附加证人签名的，她注意到，那个位置已经有三个人签了名。',\n",
       " '“我不知道父亲和这些人说的这些话。”叶文洁把材料放回原位，低声说。',\n",
       " '“怎么会不知道呢？这其中许多的谈话都是在你家里进行的，你妹妹都知道你就不知道？”',\n",
       " '“我真的不知道。”',\n",
       " '“但这些谈话内容是真实的，你要相信组织。”',\n",
       " '“我没说不是真的，可我真的不知道，所以不能签。”',\n",
       " '“叶文洁，”那名随行人员上前一步说，但又被程代表制止了。她朝文洁坐得更近些，拉起她一只冰凉的手，说：',\n",
       " '“小叶啊，我跟你交个底吧。你这个案子，弹性很大的，往低的说，知识青年受反动书籍蒙蔽，没什么大事，都不用走司法程序，参加一次学习班好好写几份检查，你就可以回兵团了；往高说嘛，小叶啊，你心里也清楚，判现行反革命是完全可以的。对于你这种政治案件，现在公检法系统都是宁左勿右，左是方法问题，右是路线问题，最终大方向还是要军管会定。当然，这话只能咱们私下说说。”',\n",
       " '随行人员说：“程代表是真的为你好，你自己看到了，已经有三个证人签字了，你签不签又有多大意义？叶文洁，你别一时糊涂啊。”',\n",
       " '“是啊，小叶，看着你这个有知识的孩子就这么毁了，心疼啊！我真的想救你，你千万要配合。看看我，我难道会害你吗？”',\n",
       " '叶文洁没有看军代表，她看到了父亲的血。“程代表，我不知道上面写的事，我不会签的。”',\n",
       " '程丽华沉默了，她盯着文洁看了好一会儿，冰冷的空气仿佛凝固了一般。然后她慢慢地将文件放回公文包，站起身，她脸上慈祥的表情仍然没有褪去，只是凝固了，仿佛戴着一张石膏面具。她就这样慈祥地走到墙角，那里放着一桶盥洗用的水，她提起桶，把里面的水一半泼到叶文洁的身上，一半倒在被褥上，动作中有一种有条不紊的沉稳，然后扔下桶转身走出门，扔下了一句怒骂：“顽固的小杂种！”',\n",
       " '看守所所长最后一个走，他冷冷地看了浑身湿透的文洁一眼，“咣”一声关上门并锁上了。',\n",
       " '在这内蒙古的严冬，寒冷通过湿透的衣服，像一个巨掌将叶文洁攥在其中，她听到自己牙齿打战的“咯咯”声，后来这声音也消失了。深入骨髓的寒冷使她眼中的现实世界变成一片乳白色，她感到整个宇宙就是一块大冰，自己是这块冰中唯一的生命体。她这个将被冻死的小女孩儿手中连火柴都没有，只有幻觉了……',\n",
       " '她置身于其中的冰块渐渐变得透明了，眼前出现了一座大楼，楼上有一个女孩儿在挥动着一面大旗，她的纤小与那面旗的阔大形成鲜明对比，那是文洁的妹妹叶文雪。自从与自己的反动学术权威家庭决裂后，叶文洁再也没有听到过她的消息，直到不久前才知道妹妹已于两年前惨死于武斗。恍惚中，挥旗的人变成了白沐霖，他的眼镜反射着楼下的火光；接着那人又变成了程代表，变成了母亲绍琳，甚至变成父亲。旗手在不断变换，旗帜在不间断地被挥舞着，像一只永恒的钟摆，倒数着她那所剩无几的生命。',\n",
       " '渐渐地旗帜模糊了，一切都模糊了，那块充满宇宙的冰块又将她封在中心，这次冰块是黑色的。',\n",
       " '《寂静的春天》1962年在美国问世时，是一本很有争议的书，它标志着人类首次关注环境问题。这是一本引发了全世界环境保护事业的书，书中描述人类可能将面临一个没有鸟、蜜蜂和蝴蝶的世界。它那惊世骇俗的关于农药危害人类环境的预言，不仅受到与之利害攸关的生产与经济部门的猛烈抨击，而且也强烈震撼了社会广大民众。 ',\n",
       " '小说作者蕾切尔·卡逊（Rachel Carson，1907年5月27日-1964年4月14日）是美国一名海洋生物学家，她凭借此小说引发了美国以至于全世界的环境保护事业。被称为“现代环保运动之母”。 ',\n",
       " '末世论是研究历史终结及其相关方面的哲学或者神学理论，这里的末世论主要是哲学角度，着眼于人类社会与自然的终结问题。蕾切尔·卡逊的《寂静的春天》所给出的末日预言，不但没有消除人们对环境污染的末日恐惧，反而加速了恐惧的发展，所以被视为宣扬了末世论。 ',\n",
       " '在“文革”的那一阶段，大部分中高级公检法机构处于军管状态，军代表对司法拥有最终决定权。 ',\n",
       " '',\n",
       " '3.\\u3000红岸之一 ',\n",
       " '不知过了多长时间，叶文洁听到了沉重的轰鸣声。这声音来自所有的方向，在她那模糊的意识中，似乎有某种巨大的机械在钻开或锯开她置身于其中的大冰块。世界仍是一片黑暗，但轰鸣声却变得越来越真实，她终于能够确定这声音的来源既不是天堂也不是地狱。她意识到自己仍闭着眼睛，便努力地睁开沉重的眼皮——首先看到了一盏灯，灯深嵌在天花板内部，被罩在一层似乎是用于防撞击的铁丝网后面，发出昏暗的光，天花板似乎是金属的。',\n",
       " '她听到有个男声在轻轻叫自己的名字。',\n",
       " '“你在发高烧。”那人说。',\n",
       " '“这是哪儿？”叶文洁无力地问，感觉声音不是自己发出的。',\n",
       " '“在飞机上。”',\n",
       " '叶文洁感到一阵虚弱，又昏睡过去，朦胧中轰鸣声一直伴随着她。时间不长，她再次清醒过来，这时麻木消失，痛苦的感觉出现了：头和四肢的关节都很痛，嘴里呼出的气是发烫的，喉咙也痛，咽下一口唾沫感觉像咽下一块火炭。',\n",
       " '叶文洁转过头，看到旁边有两个穿着和程代表一样军大衣的人，不同的是他们戴着有红五星的军棉帽，敞开的大衣露出了里面军服上的红领章，其中一名军人戴着眼镜。',\n",
       " '叶文洁发现自己也盖着一件军大衣，身上的衣服是干的，很暖和。',\n",
       " '她吃力地想支起身，居然成功了。她看到了另一边的舷窗，窗外是缓缓移去的滚滚云海，被阳光照得很刺眼；她赶紧收回目光，看到狭窄的机舱中堆满了军绿色的铁箱子，从另一个舷窗中可以看到上方旋翼的影子。她猜自己可能是在一架直升机上。',\n",
       " '“还是躺下吧。”戴眼镜的军人说，扶她重新躺下，把大衣盖好。',\n",
       " '“叶文洁，这篇论文是你写的吗？”另一名军人把一本翻开的英文杂志伸到她眼前，她看到那文章的题目是《太阳辐射层内可能存在的能量界面和其反射特性》，他把杂志的封面让她看，那是1966年的一期《天体物理学杂志》。',\n",
       " '“肯定是的，这还用证实吗？”戴眼镜的军人拿走了杂志，然后介绍说，“这位是红岸基地的雷志成政委。我是杨卫宁，基地的总工程师。离降落还有一会儿，你休息吧。”',\n",
       " '你是杨卫宁？叶文洁没有说出口，只是吃惊地看着他，发现他的表情很平静，显然不想让旁人知道他们认识。杨卫宁曾是叶哲泰的一名研究生，他毕业时叶文洁刚上大一。叶文洁现在还清楚地记得杨卫宁第一次到家里来的情形，那时他刚考上研究生，与导师谈课题方向。杨卫宁说他想搞倾向于实验和应用的课题，尽可能离基础理论远些。叶文洁记得父亲当时是这样说：我不反对，但我们毕竟是理论物理专业，你这样要求的理由呢？杨卫宁回答：我想投身于时代，做一些实际的贡献。父亲说：理论是应用的基础，发现自然规律，难道不是对时代最大的贡献？杨卫宁犹豫了一下，终于说出了真话：搞理论研究，容易在思想上犯错误。这话让父亲沉默了。',\n",
       " '杨卫宁是个很有才华的人，数学功底扎实，思维敏捷，但在不长的研究生生涯中，他与导师的关系若即若离，他们相互之间保持着敬而远之的距离。那时叶文洁与杨卫宁经常见面，也许是受父亲影响，叶文洁没有过多地注意他，至于他是否注意过自己，叶文洁就不知道了。后来杨卫宁顺利毕业，不久就与导师中断了联系。',\n",
       " '叶文洁再次虚弱地闭上眼睛后，两名军人离开了她，到一排箱子后面低声交谈。机舱很狭窄，叶文洁在引擎的轰鸣声中还是听到了他们的话——',\n",
       " '“我还是觉得这事儿不太稳妥。”这是雷志成的声音。',\n",
       " '杨卫宁反问：“那你能从正常渠道给我需要的人吗？”',\n",
       " '“唉，我也费了很大劲。这种专业从军内找不到，从地方上找，问题就更多了，你知道这项目的保密级别，首先得参军，更大的问题还是保密条例要求的在基地的隔离工作周期。那么长时间，家属随军怎么办？也得到基地里，这谁都不愿意。找到的两个合适的候选人宁肯待在五七干校也不来。当然可以硬调，但这种工作的性质，要是不安心什么都干不出来的。”',\n",
       " '“所以只能这么办。”',\n",
       " '“可这也太违反常规了。”',\n",
       " '“这个项目本来就违反常规，出了事儿我负责就是了。”',\n",
       " '“我的杨总啊，这责你负得了吗？你一头钻在技术里，‘红岸’可是与其他国防重点项目不同，它的复杂，是复杂在技术之外的。”',\n",
       " '“你这倒是实话。”',\n",
       " '※※※',\n",
       " '   ',\n",
       " '降落时已是傍晚，叶文洁谢绝了杨卫宁和雷志成的搀扶，自己艰难地走下飞机，一阵强风差点把她吹倒，风吹在仍转动的旋翼上，发出尖利的啸声。风中的森林气息文洁很熟悉，她认识这风，这风也认识她，这是大兴安岭的风。',\n",
       " '她很快听到了另一种声音，一个低沉浑厚的嗡嗡声，浑厚而有力，似乎构成了整个世界的背景，这是不远处抛物面天线在风中的声音，只有到了跟前，才能真正感受到这张天网的巨大。叶文洁的人生在这一个月里转了一个大圈又回来了——她现在是在雷达峰上。叶文洁不由得转头朝她的建设兵团连队所在的方向望去，只看到暮色中一片迷蒙的林海。',\n",
       " '直升机显然不是专为接她的，几名士兵走过来，从机舱里卸下那些军绿色的货箱，他们从她身边走过，没人看她一眼。她和雷志成、杨卫宁一行三人继续向前走去，叶文洁发现雷达峰的峰顶是这样的宽阔，在天线的下面有一小群白色建筑物，与天线相比，它们像几块精致的积木。他们正朝有两名哨兵站岗的基地大门走去，走到门前，他们停了下来。',\n",
       " '雷志成转向叶文洁，郑重地说：“叶文洁，你的反革命罪行证据确凿，将要面临的审判也是罪有应得；现在，你面前有一个立功赎罪的机会，你可以接受，也可以拒绝。”他向天线方向指了指，“这是一个国防科研基地，其中正在进行的研究项目需要你掌握的专业知识，更具体的，请杨总工程师为你介绍，你要慎重考虑。”说完他对杨卫宁点了点头，尾随搬运物资的士兵一起走进了基地。',\n",
       " '杨卫宁等别人走远了，向叶文洁示意了一下，带她走远些，显然是怕哨兵听到下面的谈话。这时，他不再隐藏自己与她的相识：“叶文洁，我可向你说清楚，这不是什么机会。我向法院军管会了解过，虽然程丽华力主重判，但具体到你的情节，刑期最多也就是十年，考虑到可能的减刑，也就是六七年的样子。而这里——”他向基地方向偏了一下头，“是最高密级的研究项目，以你的身份，走进这道门，可能……”他停了好一会儿，似乎想让天线在风中的轰鸣声加重自己的语气，“一辈子都出不来了。”',\n",
       " '“我进去。”叶文洁轻声说。',\n",
       " '杨卫宁对她这么快的回答很吃惊。“你不必这么匆忙做决定，可以先回到飞机上去，它三小时后才起飞，你要是拒绝，我送你回去。”',\n",
       " '“我不回去，我们进去吧。”叶文洁的声音仍很轻，但其中有一种斩钉截铁的坚定。现在除了死后不知是否存在的另一个世界，她最想去的地方就是这样与世隔绝的峰顶了，在这里，她有一种久违的安全感。',\n",
       " '“还是慎重些吧，你想清楚这意味着什么。”',\n",
       " '“我可以在这里待一辈子。”',\n",
       " '杨卫宁低头沉默了，他看着远方，似乎强行给叶文洁一些思考权衡的时间，叶文洁也沉默着，在风中裹紧军大衣看着远方，那里，大兴安岭已消失在浓浓的夜色中。在严寒下不可能有很多时间，杨卫宁下决心起步走向大门，走得很快，像要把叶文洁甩掉似的，但叶文洁紧跟着他，走进了红岸基地的大门。两名哨兵在他们通过后关上了两扇沉重的铁门。',\n",
       " '走了一段后，杨卫宁站住，指着天线对文洁说：“这是一个大型武器研究项目，如果成功，其意义可能比原子弹和氢弹都大。”',\n",
       " '在路过基地内最大的一幢建筑时，杨卫宁径直过去推开了门，叶文洁在门口看到了“发射主控室”的字样，迈进门，一股带着机油味的热气迎面扑来，她看到宽敞的大厅中，密集地摆放着各类仪器设备，信号灯和示波仪上的发光图形闪成一片，十多名穿军装的操作人员坐在几乎将他们埋没的一排排仪器前，仿佛是蹲守在深深的战壕中。操作口令此起彼伏，显得紧张而混乱。',\n",
       " '“这里暖和些，你先等一会儿，我去安排好你的住处就来。”杨卫宁对叶文洁说，并指指门旁边一张桌子旁的椅子让她坐。叶文洁看到，那张桌前已经坐了一个人，那是一位带手枪的卫兵。',\n",
       " '“我还是在外面等吧。”叶文洁停住脚步说。',\n",
       " '杨卫宁和善地笑笑，“你以后就是基地的工作人员了，除了少数地方，你哪里都可以去。”说完，他脸上有一种不安的表情，显然意识到了这话另一层的意思：你再也不能离开这里了。',\n",
       " '“我还是去外面吧。”叶文洁坚持说。',\n",
       " '“那……好吧。”杨卫宁看看那位并没有注意他们的卫兵，似乎理解了叶文洁，带她走出主控室，“你到这个避风的地方，我几分钟就回来，主要是找人给那个房间生上火，基地的条件现在还不太好，没有暖气。”说完快步走去。',\n",
       " '叶文洁站在主控室的门边，巨大的天线就竖立在她身后，整整占据了半个夜空。在这里，她能够清楚地听到里面传出的声音。突然，那纷乱的操作口令声消失了，主控室里一片寂静，只能隐约听到仪器设备偶尔发出的蜂鸣声，接着出现了一个压倒一切的男音：',\n",
       " '“中国人民解放军第二炮兵，红岸工程第147次常规发射，授权确认完毕，30秒倒数！”',\n",
       " '“目标类别：甲三；坐标序号：BN20197F；定位校核完毕，25秒倒数！”',\n",
       " '“发射文档号：22；附加：无；续传：无；文档最后校核完毕，20秒倒数！”',\n",
       " '“能源单元报告：正常！”',\n",
       " '“编码单元报告：正常！”',\n",
       " '“功放单元报告：正常！”',\n",
       " '“干扰监测报告：在许可范围！”',\n",
       " '“程序不可逆，15秒倒数！”',\n",
       " '一切又安静下来，十几秒钟后，随着一个警铃声响起，天线上的一盏红灯急剧闪烁起来。',\n",
       " '“发射启动！各单元注意监测！”',\n",
       " '叶文洁感到脸上有轻微的瘙痒感，她知道一个巨大的电场出现了。她仰头顺着天线所指的方向望去，看到夜空中的一缕薄云发出幽幽蓝光，那光很微弱，最初她以为是自己的幻觉，但当那缕云飘离那片空域后，云的微光就消失了，另外一缕飘入的云也同样发出光来。在主控室中，口令声又响成一片，她只能隐约听出其中的几句：',\n",
       " '“功放单元故障，3号磁控电子管烧毁！”',\n",
       " '“冗余单元投入正常！”',\n",
       " '“断点1，续传正常！”',\n",
       " '……',\n",
       " '叶文洁听到另外一种“呼啦啦”的声音，朦胧中，看到一片片黑影从山下的密林中出现，盘旋着升上夜空，她没想到严冬的森林中还有这么多的鸟儿被惊起。接着她目睹了恐怖的一幕：一个鸟群飞进了天线指向的范围，以发出幽光的那缕云为背景，她清楚地看到了群鸟纷纷从空中坠落。',\n",
       " '这一过程大约持续了十五分钟，天线上的红灯熄灭了，叶文洁皮肤上的瘙痒感也消失了，主控室中，纷乱的口令声依旧，即使在那个洪亮的男音响起后也没有停止。',\n",
       " '“红岸工程第147次发射进行完毕，发射系统关闭，红岸进入监测状态，请监测部接过系统控制权，并上传断点数据。”',\n",
       " '“请各单元组认真填写发射日志，各组长到会议室参加发射例会，完毕。”',\n",
       " '一切都沉寂下来，只有天线在风中发出的混响依旧。叶文洁看着夜空中的鸟群纷纷落回森林中。她再次仰望天线，感觉它像一只向苍穹张开的巨大手掌，拥有一种超凡脱俗的力量。她向“手掌”对着的夜空看去，并没有看到已被它打击的BN20197F号目标，在稀疏的云缕后面，只有1969年寒冷的星空。',\n",
       " '',\n",
       " '4.\\u3000三十八年后·科学边界 ',\n",
       " '汪淼觉得，来找他的这四个人是一个奇怪的组合：两名警察和两名军人，如果那两个军人是武警还算正常，但这是两名陆军军官。',\n",
       " '汪淼第一眼就对来找他的警察没有好感。其实那名穿警服的年轻人还行，举止很有礼貌，但那位便衣就让人讨厌了。这人长得五大三粗，一脸横肉，穿着件脏兮兮的皮夹克，浑身烟味，说话粗声大嗓，是最令汪淼反感的那类人。',\n",
       " '“汪淼？”那人问，直呼其名令汪淼很不舒服，况且那人同时还在点烟，头都不抬一下。不等汪淼回答，他就向旁边那位年轻人示意了一下，后者向汪淼出示了警官证，他点完烟后就直接向屋里闯。',\n",
       " '“请不要在我家里抽烟。”汪淼拦住了他。',\n",
       " '“哦，对不起，汪教授。这是我们史强队长。”年轻警官微笑着说，同时对姓史的使了个眼色。',\n",
       " '“成，那就在楼道里说吧。”史强说着，深深地吸了一大口，手中的烟几乎燃下去一半，之后竟不见吐出烟来。“你问。”他又向年轻警官偏了一下头。',\n",
       " '   ',\n",
       " '“汪教授，我们是想了解一下，最近你与‘科学边界’学会的成员有过接触，是吧？”',\n",
       " '“‘科学边界’是一个在国际学术界很有影响的学术组织，成员都是著名学者。这样一个合法的学术组织，我怎么就不能接触了呢？”',\n",
       " '“你看看你这个人！”史强大声说，“我们说它不合法了吗？我们说不让你接触了吗？”他说着，刚才吸进肚子里的烟都喷到汪淼脸上。',\n",
       " '“那好，这属于个人隐私，我没必要回答你们的问题。”',\n",
       " '“还啥都成隐私了，像你这样一个著名学者，总该对公共安全负责吧。”史强把手中的烟头扔掉，又从压扁了的烟盒里抽出一根。',\n",
       " '“我有权不回答，你们请便吧。”汪淼说着要转身回屋。',\n",
       " '“等等！”史强厉声说，同时朝旁边的年轻警官挥了一下手，“给他地址和电话，下午去走一趟。”',\n",
       " '“你要干什么！”汪淼愤怒地质问，这争吵引得邻居们也探出头来，想看看出了什么事。',\n",
       " '“史队！你说你——”年轻警官生气地将史强拉到一边，显然他的粗俗不止是让汪淼一人不适应。',\n",
       " '“汪教授，请别误会。”一名少校军官急忙上前，“下午有一个重要会议，要请几位学者和专家参加，首长让我们来邀请您。”',\n",
       " '“我下午很忙。”',\n",
       " '“这我们清楚，首长已经向超导中心领导打了招呼。这次会议不能没有您，实在不行，我们只有把会议延期等您了。”',\n",
       " '史强和他的同事没再说话，转身下楼了，两位军官看着他们走远，似乎都长出了一口气。',\n",
       " '“这人怎么这样儿。”少校小声对同事说。',\n",
       " '“他劣迹斑斑，前几年在一次劫持人质事件中，他不顾人质的死活擅自行动，结果导致一家三口惨死在罪犯手中；据说他还和黑社会打得火热，用一帮黑道势力去收拾另一帮；去年又搞刑讯逼供，使一名嫌疑人致残，因此被停职了……”',\n",
       " '“这种人怎么能进作战中心？”',\n",
       " '“首长点名要他，应该有什么过人之处吧。不过，对他限制挺严，除了公安方面的事务，几乎什么都不让他知道。”',\n",
       " '作战中心？那是什么？汪淼不解地看着面前的两位军官。',\n",
       " '※※※',\n",
       " '接汪淼的汽车驶进了城市近郊的一座大院，从那只有门牌号码没有单位名牌的大门，汪淼知道这里是军方而不是警方的地盘。',\n",
       " '会议是在一个大厅里举行的，汪淼一进去就对这里的纷乱吃惊不小。大厅周围是一圈胡乱安放的电脑设备，有的桌子上放不下就直接搁地板上，电线和网线纠缠着散在地上；一大摞网络交换机没有安在机架内，而是随手堆放在服务器上；有好几个投影仪的大屏幕，在大厅的角落里呈不同角度随意立着，像吉普赛人的帐篷；烟雾像晨雾般在半空浮了一层……汪淼不知道这是否就是那名军官所说的作战中心，有一点他可以肯定：这里在处理的事情，已经让人们顾不上其他了。',\n",
       " '临时拼凑的会议桌上也是堆满了文件和杂物，与会者大多神情疲惫，衣服皱巴巴的，有领带的都扯开了，好像熬了一夜。主持会议的是一位叫常伟思的陆军少将，与会者有一半是军人。经过简单的介绍，他知道还有少部分警方人员，其他的人都是和他一样参加会议的专家学者，其中有几位还是很有名望的科学家，而且是研究基础科学的。',\n",
       " '令他感到意外的是还有四个外国人，这些人的身份令他大吃一惊：其中的两个人也是军人，分别是美军空军上校和英国陆军上校，职务是北约联络员；另外两人居然是美国中央情报局的官员，在这里的职务是什么观察员。从所有人的脸上，汪淼都读出了一句话：我们已经尽力了，快他妈的结束吧！',\n",
       " '汪淼看到了史强，他倒是一反昨天的粗鲁，向汪淼打招呼，但那一脸傻笑让汪淼愉快不起来。他不想挨史强坐，但也只有那一个空位，他只好坐过去，屋里本来已经很浓的烟味更加重了。',\n",
       " '发文件时，史强凑近汪淼说：“汪教授，你好像是在研究什么……新材料？”',\n",
       " '“纳米材料。”汪淼简单地回答。',\n",
       " '“我听说过，那玩意儿强度很高，不会被用于犯罪吧？”从史强那带有一半调侃的表情上，汪淼看不出他是不是开玩笑。',\n",
       " '“什么意思？”',\n",
       " '“呵，听说那玩意儿一根头发丝粗就能吊起一辆大卡车，犯罪分子要是偷点儿去做把刀，那一刀就能把一辆汽车砍成两截吧。”',\n",
       " '“哼，根本不用做成刀，用那种材料做一根只有头发丝百分之一粗细的线，拦在路上，就能把过往的汽车像切奶酪那样切成两半……啥不能用于犯罪？刮鱼鳞的刀都能！”',\n",
       " '史强把面前的文件从袋中抽出一半又塞了回去，显然没了兴趣。“说得对，鱼都能犯罪呢！我办过一个杀人案，一个娘们儿把她丈夫的那玩意儿割下来了。知道用的是什么？冰箱里冷冻的罗非鱼！鱼冻硬后，背上的那排刺就跟一把快刀似的……”',\n",
       " '“我没兴趣，怎么，让我来开会就是为这事儿？”',\n",
       " '“鱼？纳米材料？不、不，与那些都没关系。”史强把嘴凑到汪淼耳边，“别给这帮家伙好脸，他们歧视咱们，只想从咱们这里掏情报，但什么都不告诉咱们。像我，在这儿混了一个多月，还和你一样什么都不知道。”',\n",
       " '“同志们，会议开始。”常伟思将军说，“在全球各战区，我们这里现在成为焦点。首先把当前情况向与会的同志们介绍一下。”',\n",
       " '“战区”这个不寻常的术语令汪淼迷惑，他还注意到，首长好像并没有打算向他这样的新人介绍来龙去脉，这倒是印证了史强的话。在常将军这简短的开场白中，他两次提到了“同志们”，汪淼看看对面的两名北约军人和两个美国中情局官员，感觉将军似乎漏掉了“先生们”。',\n",
       " '“他们也是同志，反正这边的人都是这么称呼的。”史强低声地对汪淼说，同时用手中的烟指了指那四个外国人。',\n",
       " '在迷惑的同时，汪淼对史强的观察力留下了些印象。',\n",
       " '“大史，你把烟熄了，这儿的烟味够浓了。”常伟思说，低头翻着文件。',\n",
       " '史强拿着刚点着的烟四下看看，没找到烟灰缸，就“吱啦”一声扔到茶杯里了。他抓住这个机会举手要求发言，没等常伟思表态就大声说道：“首长，我提个要求，以前提过的——信息对等！”',\n",
       " '常伟思将军抬起头，“没有任何一个军事行动是信息对等的，这点也请到会的专家学者们谅解，我们不可能给你们介绍更多的背景资料。”',\n",
       " '“但我们不一样。”史强说，“警方从作战中心成立之初就一直参与，可直到现在，我们连这个机构到底是干什么的都不知道。而且，你们正在把警方排挤出去，你们一步步熟悉我们的工作，然后把我们一个个赶走。”',\n",
       " '与会的另外几名警官都在低声制止史强。史强敢对常伟思这样级别的首长这么说话，汪淼有些吃惊，而后者的反击更犀利。',\n",
       " '“我说大史，现在看来，你在部队上的老毛病还没改。你能代表警方吗？你因为自己的恶劣行为已被停职好几个月了，马上就要被清除出公安队伍。我调你来，是看重你在城市警务方面的经验，你要珍惜这次机会。”',\n",
       " '大史用粗嗓门说：“那我是戴罪立功了？你们不是说那都是些歪门邪道的经验吗？”',\n",
       " '“但有用。”常伟思对史强点点头，“有用就行，现在顾不上那么多了，这是战争时期。”',\n",
       " '“什么都顾不了了，”一位中情局的情报官员用标准的普通话说，“我们不能再用常规思维。”',\n",
       " '那位英军上校显然也能听懂中文，他点点头，“To be or not to be……”',\n",
       " '“他说什么？”史强问汪淼。',\n",
       " '“没什么。”汪淼机械地回答。这些人似乎在梦呓，战争时期？战争在哪儿？他扭头望向大厅的落地窗，透过窗子可以看到远处大院外面的城市：春天的阳光下，街道上车流如织；草坪上有人在遛狗，还有几个孩子在玩耍……',\n",
       " '里面和外面的世界，哪个更真实？',\n",
       " '常将军讲道：“最近，敌人的攻击明显加强了，目标仍是科学界高层，请你们先看一下文件中的那份名单。”',\n",
       " '汪淼抽出文件中最上面的那张纸，是用大号字打印的，名单显然拟得很仓促，中文和英文姓名都有。',\n",
       " '“汪教授，看到这份名单，您有什么印象？”常伟思看着汪淼问。',\n",
       " '“我知道其中的三人，都是物理学最前沿的著名学者。”汪淼答道，有些心不在焉，他的目光锁定在最后一个名字上，在他的潜意识中，那两个字的色彩与上面几行字是不同的。怎么会在这里看到她的名字？她怎么了？',\n",
       " '“认识？”大史用一根被烟熏黄的粗指头指着文件上的那个名字问。见汪淼没有反应，他迅速作出反应，道：“呵，不太认识。想认识？”',\n",
       " '现在，汪淼知道常伟思把他以前的这个战士调来是有道理的，这个外表粗俗的家伙，眼睛跟刀子一样。他也许不是个好警察，但确实是个狠角色。',\n",
       " '※※※',\n",
       " '那是一年前，汪淼是“中华二号”高能加速器  项目纳米构件部分的负责人。那天下午在良湘的工地上，一次短暂的休息中，他突然被眼前的一幅构图吸引了。作为一名风景摄影爱好者，现实的场景经常在他眼中形成一幅幅艺术构图。构图的主体就是他们正在安装的超导线圈，那线圈有三层楼高，安装到一半，看上去是一个由巨大的金属块和乱麻般的超低温制冷剂管道组成的怪物，仿佛一堆大工业时代的垃圾，显示出一种非人性的技术的冷酷和钢铁的野蛮。就在这金属巨怪前面，出现了一个年轻女性纤细的身影。这构图的光线分布也很绝：金属巨怪淹没在临时施工顶棚的阴影里，更透出那冷峻、粗糙的质感；而一束夕阳金色的光，透过顶棚的孔洞正好投在那个身影上，柔和的暖光照着她那柔顺的头发，照着工作服领口上白皙的脖颈，看上去就像一场狂暴的雷雨后，巨大的金属废墟上开出了一朵娇柔的花……',\n",
       " '“看什么看，干活儿！”',\n",
       " '汪淼吓了一跳，然后发现纳米研究中心主任说的不是他，而是一名年轻工程师，后者也和自己一样呆呆地望着那个身影。汪淼从艺术中回到现实，发现那位女性不是一般的工作人员，因为总工程师陪同着她，在向她介绍着什么，一副很尊敬的样子。',\n",
       " '“她是谁？”汪淼问主任。',\n",
       " '“你应该知道她的，”主任说，用手划了一大圈，“这个投资二百亿的加速器建成后，第一次运行的可能就是验证她提出的一个超弦模型。要说在论资排辈的理论研究圈子，本来轮不到她的，可那些老家伙不敢先来，怕丢人，就让她捡了个便宜。”',\n",
       " '“什么？杨冬是……女的？！”',\n",
       " '“是的，我们也是在前天见到她时才知道。”主任说。',\n",
       " '那名工程师问：“她这人是不是有什么心理障碍，要不怎么会从来不上媒体呢？别像是钱钟书似的，到死大家也没能在电视上看上一眼。”',\n",
       " '“可我们也不至于不知道钱钟书的性别吧？我觉得她童年一定有什么不寻常的经历，以致得了自闭症。”汪淼说，多少有一些酸葡萄心理。',\n",
       " '杨冬和总工程师走过来，在经过时她对他们微笑着点点头，没说一句话，但汪淼记住了她那清澈的眼睛。',\n",
       " '当天晚上汪淼坐在书房里，欣赏着挂在墙上的自己最得意的几幅风景摄影，他的目光落在一幅塞外风光上——那是一个荒凉的山谷，雪山从山谷的尽头露出一抹白；山谷的这一端，半截沧桑的枯木占据了几乎三分之一的画面。汪淼在想象中把那个萦绕在他脑海中的身影叠印到画面上，让她位于山谷的深处，看上去很小很小；这时汪淼惊奇地发现，整个画面苏醒过来，仿佛照片中的世界认出了那个身影，仿佛这一切本来就是为她而存在。他又依次在想象中将那个身影叠印到另外几幅作品上，有时还将她那双眼睛作为照片上空旷苍穹的背景，那些画面也都苏醒过来，展现出一种汪淼从未想象过的美。以前，汪淼总觉得自己的摄影作品缺少某种灵魂；现在他知道了，缺的是她。',\n",
       " '※※※',\n",
       " '“名单上的这些物理学家，在不到两个月的时间里，先后自杀。”常伟思说。',\n",
       " '晴天霹雳，汪淼的大脑一片空白。后来这空白中渐渐有了图像，那是他那些黑白风景照片，照片中的大地没有了她的身影，天空抹去了她的眼睛，那些世界死了。',\n",
       " '“是……什么时候？”汪淼呆呆地问。',\n",
       " '“在不到两个月的时间里。”常将军重复道。',\n",
       " '“你是指最后一位吧。”坐在汪淼旁边的大史得意地说，然后压低声音，“她是最后一位自杀者，前天晚上，服过量安眠药。她死得很顺溜，没有痛苦。”',\n",
       " '刹那间，汪淼居然对大史有了那么一丝感激。',\n",
       " '“为什么？”汪淼问，那些照片上死去的风景画仍在他的脑海中幻灯似的循环浮现。',\n",
       " '常伟思回答道：“现在能肯定的只有一点：促使他们自杀的原因是相同的。但原因本身在这里很难说清，也可能对我们这些非专业人士根本就说不清。文件中附加了他们遗书的部分内容，各位会后可以仔细看看。”',\n",
       " '汪淼翻翻那些遗书的复印件，都是长篇大论。',\n",
       " '“丁仪博士，您能否把杨冬的遗书给汪教授看一下？她的最简短，也最有概括性。”',\n",
       " '    ',\n",
       " '《球状闪电》封面 ',\n",
       " '《球状闪电》描述了在地球遭受威胁时，球状闪电被纳入到“新概念武器”开发计划，成为决定祖国命运存亡的关键武器的故事。 ',\n",
       " '那个一直低着头沉默的人半天才有所反应，掏出一个白色的信封隔着桌子递给汪淼，大史在旁边低声说：“他是杨冬的男友。”汪淼这才想起自己在良湘的高能加速器工地中也见过丁仪，他是理论组的成员，这名物理学家因在对球状闪电  的研究中发现宏原子  而闻名于世。汪淼从信封中抽出一片散发出清香的东西，形状不规则，不是纸，竟是一片白桦树皮，上面有一行娟秀的字：',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '一切的一切都导向这样一个结果：物理学从来就没有存在过，将来也不会存在。我知道自己这样做是不负责任的，但别无选择。 ',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '连签字都没有，她就走了。',\n",
       " '“物理学……不存在？”汪淼茫然四顾。',\n",
       " '常将军合上文件夹，“有一些相关的具体信息与世界上三台新的高能加速器建成后取得的实验结果有关，很专业，我们就不在这里讨论了。我们首先要调查的是‘科学边界’学会。联合国教科文组织将2005年定为世界物理年，这个组织就是在这一年国际物理学界频繁的学术会议和交流活动中逐渐诞生的，是一个松散的国际性学术组织。丁博士，您是理论物理专业的，能进一步介绍一下它的情况吗？”',\n",
       " '丁仪点点头说：“我与‘科学边界’没有任何直接联系，不过这个组织在学术界很有名。它的宗旨是：自上个世纪下半叶以来，物理学古典理论中的简洁有力渐渐消失了，理论图像变得越来越复杂、模糊和不确定，实验验证也越来越难，这标志着物理学的前沿探索似乎遇到了很大的障碍和困难。‘科学边界’试图开辟一条新的思维途径，简单地说就是试图用科学的方法找出科学的局限性，试图确定科学对自然界的认知在深度和精度上是否存在一条底线——底线之下是科学进入不了的。现代物理学的发展，似乎隐隐约约地触到了这条底线。”',\n",
       " '“很好。”常伟思说，“据我们了解，这些自杀的学者大部分与‘科学边界’有过联系，有些还是它的成员。但没有发现诸如邪教精神控制或使用违法药物这类的犯罪行为。也就是说，即使‘科学边界’对那些学者产生过影响，也是通过合法的学术交流途径。汪教授，他们最近与您有联系，我们想了解一些情况。”',\n",
       " '大史粗声粗气地开口说：“包括联系人的姓名、见面地点和时间、谈话内容，如果交换过文字资料或电子邮件的话……”',\n",
       " '“大史！”常伟思厉声制止了他。',\n",
       " '“不吱声没人拿你当哑巴！”旁边一位警官探过身去对大史低声说，后者拿起桌上的茶杯，看到里面的烟头后，“咚”的一声又放下了。',\n",
       " '大史又令汪淼像吃了苍蝇一样难受，刚才那一丝感激消失得无影无踪。但他还是克制着回答了这个问题：“我与‘科学边界’的接触是从认识申玉菲开始的，她是一名日籍华裔物理学家，现在为一家日资公司工作，就住在这个城市。她曾在三菱电机的一家实验室从事纳米材料研究，我们是在今年年初的一次技术研讨会上认识的。通过她，又认识了几位物理专业的朋友，都是‘科学边界’的成员，国内国外的都有。和他们的交往时，谈的都是一些很……怎么说呢，很终极的问题，主要就是丁博士刚才提到的科学底线的问题。',\n",
       " '“我一开始对这些问题没有太大的兴趣，只是作为消遣。我是搞应用研究的，在这方面水平不高，主要是听他们讨论和争论。这些人思想都很深刻，观点新颖，自己感觉同他们交流，思想开阔了许多，渐渐变得很投入了。但讨论的话题仅限于此，都是天马行空的纯理论，没有什么特别的。他们曾邀请我加入‘科学边界’，但那样的话，参加这样的研讨会就变成了一项义务，我因为精力有限就谢绝了。”',\n",
       " '“汪教授，我们希望您接受邀请，加入‘科学边界’学会，这也是我们今天请您来的主要目的。”常将军说，“我们希望能通过您这个渠道，得到一些这个组织的内部信息。”',\n",
       " '“您是说让我去卧底吗？”汪淼不安地问。',\n",
       " '“哇哈哈，卧底！”大史大笑一声。',\n",
       " '常伟思责备地看了大史一眼，对汪淼说：“只是提供一些情况，我们也没有别的渠道。”',\n",
       " '汪淼摇摇头，“对不起，首长。我不能干这事。”',\n",
       " '“汪教授，‘科学边界’是一个由国际顶尖学者构成的组织，对它的调查是一件极其复杂和敏感的事，我们真的是如履薄冰。没有知识界的帮助，我们寸步难行，所以才提出了这个唐突的要求，希望您能理解。不过我们也尊重您的意愿，如果不同意，我们也是能够理解的。”',\n",
       " '“我……工作很忙，也没有时间。”汪淼推托道。',\n",
       " '常伟思点点头，“好的，汪教授，那我们就不再耽误您的时间了，谢谢您能来参加这次会议。”',\n",
       " '汪淼愣了几秒钟，才明白他该离开了。',\n",
       " '常伟思礼貌地把汪淼送到会议室门口时，大史在后面大声说：“这样挺好，我压根儿就不同意这个方案。已经有这么多书呆子寻了短见，让他去不是‘肉包子打狗’吗？”',\n",
       " '汪淼返身回去，走到大史身旁，努力克制着自己的愤怒，“你这么说话实在不像一名合格的警官。”',\n",
       " '“我本来就不是。”',\n",
       " '“那些学者自杀的原因还没有搞清楚。你不该用这么轻蔑的口气谈论他们，他们用自己的智慧为人类社会做出的贡献，是任何人都不可替代的。”',\n",
       " '“你是说他们比我强？”大史在椅子上仰头看着汪淼，“我总不至于听人家忽悠几句就去寻短见。”',\n",
       " '“那你是说我会？”',\n",
       " '“总得对您的安全负责吧。”大史看着汪淼，又露出他招牌式的傻笑。',\n",
       " '“在那种情况下我比你要安全得多，你应该知道，一个人的鉴别能力是和他的知识成正比的。”',\n",
       " '“那不见得，像您这样的……”',\n",
       " '“大史，你要再多说一句，也从这里出去好了！”常伟思严厉地呵斥道。',\n",
       " '“没关系，让他说，”汪淼转向了常将军，“我改变主意了，决定按您的意思加入‘科学边界’。”',\n",
       " '“很好，”大史连连点头，“进去后机灵点儿，有些事顺手就能做，比如瞄一眼他们的电脑，记个邮件地址或网址什么的……”',\n",
       " '“够了！够了！你误会了，我不是去卧底，只是想证明你的无知和愚蠢！”',\n",
       " '“如果您过一阵儿还活着，那自然也就证明了。不过恐怕……嘿嘿。”大史仰着头，傻笑变成了狞笑。',\n",
       " '“我当然会一直活下去，但实在不想再见到你这号人了！”',\n",
       " '※※※',\n",
       " '常伟思一直把汪淼送下了楼梯，并安排车送他，在道别时说：“史强就那种脾气，其实他是一名很有经验的刑警和反恐专家。二十多年前，他曾是我连里的一名战士。”',\n",
       " '走到车前，常伟思又说：“汪教授，你一定有很多问题要问。”',\n",
       " '“刚才您说的那些，与军方有什么关系？”',\n",
       " '“战争与军方当然有关系。”',\n",
       " '汪淼迷惑地看看周围明媚春光中的一切，“可战争在哪儿？现在全球一处热点都没有，应该是历史上最和平的年代了。”',\n",
       " '常伟思露出了高深莫测的笑容：“你很快就会知道一切的，所有人都会知道。汪教授，你的人生中有重大的变故吗？这变故突然完全改变了你的生活，对你来说，世界在一夜之间变得完全不同。”',\n",
       " '“没有。”',\n",
       " '“那你的生活是一种偶然，世界有这么多变幻莫测的因素，你的人生却没什么变故。”',\n",
       " '汪淼想了半天还是不明白，“大部分人都是这样嘛。”',\n",
       " '“那大部分人的人生都是偶然。”',\n",
       " '“可……多少代人都是这么平淡地过来的。”',\n",
       " '“都是偶然。”',\n",
       " '汪淼摇头笑了起来，“得承认今天我的理解力太差了，您这岂不是说……”',\n",
       " '“是的，整个人类历史也是偶然，从石器时代到今天都没什么重大变故，真幸运。但既然是幸运，总有结束的一天；现在我告诉你，结束了，做好思想准备吧。”',\n",
       " '汪淼还想问下去，但将军与他握手告别，阻止了他下面的问题。',\n",
       " '上车后，司机开口问汪淼家的地址，汪淼告诉他后，随口问道：“哦，接我来的不是你？我看车是一样的。”',\n",
       " '“不是我，我是去接丁博士的。”',\n",
       " '汪淼心里一动，便向司机打听丁仪的住处，司机告诉了他。当天晚上，他就去找丁仪。',\n",
       " '高能加速器即微粒子加速器，是高能物理实验所使用的主要工具。它可以将粒子加速到接近光速，然后使其碰撞，便于科学家观看其产生的效果。 ',\n",
       " '球状闪电通常是在强雷暴时出现的外观呈球状的一种奇异闪电，直径一般为10～20cm。这是一个真实存在的物理现象。 ',\n",
       " '宏原子是刘慈欣在《球状闪电》中，基于他自己对球状闪电的理解，从而虚构出来的对球状闪电原子构成的一种解释。 ',\n",
       " '',\n",
       " '5.\\u3000台球 ',\n",
       " '推开丁仪那套崭新的三居室的房门，汪淼闻到了一股酒味，看到丁仪躺在沙发上，电视开着，他的双眼却望着天花板。汪淼四下打量了一下，看到房间还没怎么装修，也没什么家具和陈设，宽大的客厅显得很空，最显眼的是客厅一角摆放的一张台球桌。',\n",
       " '对汪淼的不请自来，丁仪倒没表示反感，他显然也想找人说话。',\n",
       " '“这套房子是三个月前买的，”丁仪说，“我买房子干什么？难道她真的会走进家庭？”他带着醉意笑着摇摇头。',\n",
       " '“你们……”汪淼想知道杨冬生活中的一切，但又不知该如何问。',\n",
       " '“她像一颗星星，总是那么遥远，照到我身上的光也总是冷的。”丁仪走到窗前看着夜空，像在寻找那颗已逝去的星辰。',\n",
       " '汪淼也沉默下来。很奇怪，他现在就是想听一听她的声音，一年前那个夕阳西下的时刻，她同他对视的那一瞬间没有说话，他从来没有听到过她的声音。',\n",
       " '丁仪一挥手，像要赶走什么，将自己从这哀婉的思绪中解脱出来。“汪教授，你是对的，别跟军方和警方纠缠到一块儿，那是一群自以为是的白痴。那些物理学家的自杀与‘科学边界’没有关系，我对他们解释过，可解释不清。”',\n",
       " '“他们好像也做过一些调查。”',\n",
       " '“是，而且这种调查还是全球范围的，那他们也应该知道，其中的两人与‘科学边界’没有任何来往，包括——杨冬。”丁仪说出这个名字时显得很吃力。',\n",
       " '“丁仪，你知道，我现在也卷进这件事里了。所以，关于使杨冬做出这种选择的原因，我很想知道，我想你一定知道一些。”汪淼笨拙地说道，试图掩盖他真正的心迹。',\n",
       " '“如果知道了，你只会卷得更深。现在你只是人和事卷进来了，知道后连精神也会卷进来，那麻烦就大了。”',\n",
       " '“我是搞应用研究的，没有你们理论派那么敏感。”',\n",
       " '“那好吧，打过台球吗？”丁仪走到了台球桌前。',\n",
       " '“上学时随便玩过几下。”',\n",
       " '“我和她很喜欢打，因为这让我们想到了加速器中的粒子碰撞。”丁仪说着拿起黑白两个球，将黑球放到洞旁，将白球放到距黑球仅十厘米左右的位置，问汪淼，“能把黑球打进去吗？”',\n",
       " '“这么近谁都能。”',\n",
       " '“试试。”',\n",
       " '汪淼拿球杆，轻击白球，将黑球撞入洞内。',\n",
       " '“很好，来，我们把球桌换个位置。”丁仪招呼一脸迷惑的汪淼，两人抬起沉重的球桌，将它搬到客厅靠窗的一角。放稳后，丁仪从球袋内掏出刚才打进去的黑球，将它放到洞边，又拾起那个白球，再次放到距黑球十厘米左右的地方，“这次还能打进去吗？”',\n",
       " '“当然。”',\n",
       " '“打吧。”',\n",
       " '汪淼再次轻而易举地将黑球打入洞内。',\n",
       " '“搬。”丁仪挥手示意，两人再次抬起球桌，搬到客厅的第三个角，丁仪又将黑白两个球摆放到同样的位置，“打吧。”',\n",
       " '“我说，我们……”',\n",
       " '“打吧。”',\n",
       " '汪淼无奈地笑笑，第三次将黑球击入洞内。',\n",
       " '他们又搬了两次台球桌，一次搬到了客厅靠门的一角，最后一次搬回了原位。丁仪又两次将黑白球摆到洞前的位置，汪淼又两次将黑球击入洞内。这时两人都有些出汗了。',\n",
       " '“好了，实验结束，让我们来分析一下结果。”丁仪点上一支烟说，“我们总共进行了五次试验，其中四次在不同的空间位置和不同的时间，两次在同一空间位置但时间不同。您不对结果震惊吗？”他夸张地张开双臂，“五次，撞击试验的结果居然都一样！”',\n",
       " '“你到底想表达什么？”汪淼喘着气问。',\n",
       " '“你现在对这令人难以置信的结果做出解释，用物理学语言。”',\n",
       " '“这……在五次试验中，两个球的质量是没有变化的；所处位置，当然是以球桌面为参照系来说，也没有变化；白球撞击黑球的速度向量也基本没有变化，因而两球之间的动量交换也没有变化，所以五次试验中黑球当然都被击入洞中。”',\n",
       " '丁仪拿起撂在地板上的一瓶白兰地，把两个脏兮兮的杯子分别倒满，递给汪淼一杯，后者谢绝了。“该庆祝一下，我们发现一个伟大定律：物理规律在时间和空间上是均匀的。人类历史上所有物理学理论，从阿基米德原理到弦论  ，以至人类迄今为止的一切科学发现和思想成果，都是这个伟大定律的副产品，与我们比，爱因斯坦和霍金才真是搞应用的俗人。”',\n",
       " '“我还是不明白你想表达什么。”',\n",
       " '    ',\n",
       " '艾萨克·阿西莫夫 ',\n",
       " '美国科幻小说黄金时代的代表人物之一。他一生著述颇丰，曾获得科幻界最高荣誉雨果奖和星云终身成就“大师奖”。 ',\n",
       " '“想象另一种结果：第一次，白球将黑球撞入洞内；第二次，黑球走偏了；第三次，黑球飞上了天花板；第四次，黑球像一只受惊的麻雀在房间里乱飞，最后钻进了您的衣袋；第五次，黑球以接近光速的速度飞出，把台球桌沿撞出一个缺口，击穿了墙壁，然后飞出地球，飞出太阳系，就像阿西莫夫描写的那样  。这时您怎么想？”',\n",
       " '丁仪盯着汪淼，后者沉默许久才问：“这事真的发生了，是吗？”',\n",
       " '',\n",
       " '',\n",
       " '丁仪将手中的两杯酒都仰头灌下去，两眼直勾勾地看着台球桌，仿佛那是个魔鬼，“是的，发生了。近年来，基础理论研究的实验验证条件渐渐成熟，有三个昂贵的‘台球桌’被造了出来，一个在北美，一个在欧洲，还有一个你当然知道，在中国良湘，你们纳米中心从那里赚了不少钱。',\n",
       " '“这些高能加速器将实验中粒子对撞的能量提高了一个数量级，这是人类以前从未达到过的。在新的对撞能级下，同样的粒子，同样的撞击能量，一切试验条件都相同，结果却不一样。不但在不同的加速器上不一样，在同一加速器不同时间的试验中也不一样，物理学家们慌了，把这种相同条件的超高能撞击试验一次次地重复，但每次的结果都不同，也没有规律。”',\n",
       " '“这意味着什么呢？”汪淼问，看到丁仪盯着自己不做声，他又补充道，“哦，我搞纳米，也接触物质微观结构，但比起你们来要浅好几个层次，请指教一下。”',\n",
       " '“这意味着物理规律在时间和空间上不均匀。”',\n",
       " '“这又意味着什么呢？”',\n",
       " '“往下您应该能推论出来吧，那个将军都想出来了，他真是个聪明人。”',\n",
       " '汪淼看着窗外沉思着，外面城市的灯海一片灿烂，夜空中的星星被淹没得看不见了。',\n",
       " '“这就意味着宇宙普适的物理规律不存在，那物理学……也不存在了。”汪淼从窗外收回目光说。',\n",
       " '“‘我知道自己这样做是不负责任的，但别无选择。’”丁仪紧接着说，“这是她遗书的后半部分，您无意中刚说出了前半部分，现在多少能够理解她吧。”',\n",
       " '汪淼从台球桌上拿起刚才他打过五次的那个白球，抚摸了一会儿轻轻放下，“这对一个前沿理论的探索者确实是个灾难。”',\n",
       " '“在理论物理这个领域要想有所建树，需要一种宗教般的执著，这很容易把人引向深渊。”',\n",
       " '告辞时，丁仪给了汪淼一个地址。“你如果有空，拜托去看看杨冬的母亲。杨冬一直和她住在一起，女儿是她生活的全部，现在就一个人了，很可怜。”',\n",
       " '汪淼说：“丁仪，你知道得显然比我多，就不能再透露一点吗？你真的相信物理规律在时空上不均匀？”',\n",
       " '“我什么都不知道……”',\n",
       " '丁仪与汪淼对视了好长时间，最后说：“这是个问题。”',\n",
       " '汪淼知道，他不过是接下了那位英军上校的话：生存还是死亡，这是个问题。',\n",
       " '弦理论是发展中的理论物理学的一支，结合量子力学和广义相对论为量子引力。其雏形是在1968年由Gabriele Veneziano发现。弦理论用一段段“能量弦线”作最基本单位以说明世界上所有物质结构，大至星际银河，小至电子、质子及夸克一类的基本粒子都由这一维的“能量线”所组成。 ',\n",
       " '此处描述场景指阿西莫夫的短篇科幻小说《台球》。该小说由记者埃德的视角，描述了理论科学家詹姆斯·普利斯与实践科学家爱德华·布鲁姆之间关于两场论、反引力装置的冲突。在詹姆斯·普利斯推翻爱德华·布鲁姆反引力装置的实验中，出现了小球不遵循物理规则运行的情况。 ',\n",
       " '',\n",
       " '6.\\u3000射手和农场主 ',\n",
       " '第二天是周末，汪淼反而起得很早，带上相机骑着自行车出去了。作为一名摄影爱好者，他最向往的题材是人迹罕至的荒野，但人到中年，已经没有精力进行这种奢侈的享受了，大多数时间只能在城市里拍风景了。他有意无意地选取城市中那些散发着蛮荒气息的角落，如公园中干涸的湖底、建筑工地上翻出的新土、钻出水泥缝隙的野草等。为了消除背景上城市的俗艳色彩，他只使用黑白胶片，没想到竟自成一派，渐渐小有名气，作品入选了两次大影展，还加入了摄影家协会。每次出去拍摄，他就这样骑着自行车在城市里随意乱转，捕捉着灵感和他需要的构图，有时一转就是一整天。',\n",
       " '今天，汪淼的感觉有些异样。他的摄影以古典风格的沉稳凝重见长，但今天，他很难再找到创造这种构图所需要的稳定感，在他的感觉中，这座正在晨曦中苏醒的城市似乎建立在流沙上，它的稳定是虚幻的。在刚过去的那一夜，那两颗台球一直占据着他长长的梦境，它在黑色的空间中无规则地乱飞，在黑色的背景上黑球看不见，它只有在偶尔遮挡白球时才显示一下自己的存在。',\n",
       " '难道物质的本原真的是无规律吗？难道世界的稳定和秩序，只是宇宙某个角落短暂的动态平衡？只是混乱的湍流中一个短命的旋涡？',\n",
       " '不知不觉中，他已骑到了新落成的CCTV大厦脚下。他停下车，坐到路边，仰望这Ａ字形的巍峨建筑，试图找回稳定的感觉，顺着大厦在朝阳中闪烁的尖顶的指向，他向深不见底的蓝色苍穹望去，脑海中突然浮现出两个词：射手、农场主。',\n",
       " '在“科学边界”的学者们进行讨论时，常用到一个缩写词：SF，它不是指科幻，而是上面那两个词的缩写。这源自两个假说，都涉及宇宙规律的本质。',\n",
       " '“射手”假说：有一名神枪手，在一个靶子上每隔十厘米打一个洞。设想这个靶子的平面上生活着一种二维智能生物，它们中的科学家在对自己的宇宙进行观察后，发现了一个伟大的定律：“宇宙每隔十厘米，必然会有一个洞。”它们把这个神枪手一时兴起的随意行为，看成了自己宇宙中的铁律。',\n",
       " '“农场主假说”则有一层令人不安的恐怖色彩：一个农场里有一群火鸡，农场主每天中午十一点来给它们喂食。火鸡中的一名科学家观察这个现象，一直观察了近一年都没有例外，于是它也发现了自己宇宙中的伟大定律：“每天上午十一点，就有食物降临。”它在感恩节早晨向火鸡们公布了这个定律，但这天上午十一点食物没有降临，农场主进来把它们都捉去杀了。',\n",
       " '汪淼感到脚下的路面像流沙般滑动，Ａ字形大厦仿佛摇晃起来，他赶紧收回目光。',\n",
       " '※※※',\n",
       " '仅仅是为了摆脱不安，汪淼强迫自己拍完了一个胶卷，午饭前回到了家。妻子带着孩子出去玩，中午不回来了。往常，汪淼一定会迫不及待地把胶卷冲出来，但今天他一点兴致都没有。简单地吃过午饭后，他倒头便睡，由于昨天夜里没睡好，一觉睡醒后都快五点了。他这时才想起上午拍的胶卷，便钻到那间由壁橱改成的狭窄暗室里去冲洗。',\n",
       " '胶片很快冲出来了，他开始查看哪张值得放大洗成照片，在第一张就发现了一件离奇的事。这张拍的是一个大商场外的一小片草地，他看到底片正中有一行白色的东西，细看是一排数字：1200:00:00。',\n",
       " '第二张底片上也有数字：1199:49:33。',\n",
       " '整卷胶片每张底片上都有小小的一排数字！',\n",
       " '第三张：1199:40:18；第四张：1199:32:07；第五张：1199:28:51；第六张：1199:15:44；第七张：1199:07:38；第八张：1198:53:09',\n",
       " '……',\n",
       " '第三十四张：1194:50:49；第三十六张也是最后一张：1194:16:37。',\n",
       " '汪淼立刻想到是胶卷的问题。他使用的是1988年产的莱卡M2型相机，全机械手动，没有任何自动化功能，更不可能往胶片上叠印日期一类的数字。仅凭其品质卓绝的镜头和机械机构，即使在数码时代，也是专业相机中的贵族。',\n",
       " '重新查看每张底片，汪淼很快发现了这些数字的第一个诡异之处：它们自动适应背景。如果背景是黑色，数字则为白色，白色背景上的数字就是黑色，似乎是为了形成最大的反差便于观察者看清。当汪淼再看第十六张底片时，心跳加快了，感到暗室中有一股寒气沿着脊背升上来：',\n",
       " '这张拍的是以一面老墙为背景的一棵枯树，老墙斑驳一片，在照片上黑白相间。在这样的背景上，那行数字以正常的位置无论是黑是白都不可能显示清楚，但它竟竖了起来，且弯曲自身，沿着枯树深色的树身呈白色显示，看上去仿佛是附着在枯树上的一条细蛇！',\n",
       " '汪淼开始研究那些数字的数学关系，起初他以为是某种编号，但每组数字的间隔并不相同，他很快明白这是以小时、分、秒为单位的计时。他拿出了拍摄笔记，上面详细记录了每张照片的拍摄时间，精确到分。他发现两张照片上计时的差值与它们实际拍摄的时间间隔是一致的。很明显，这卷胶片上反向记录了某个以现实的速度流逝的时间。汪淼马上明白了它是什么。',\n",
       " '一个倒计时。',\n",
       " '倒计时从1200小时开始，到现在还剩余1194小时。',\n",
       " '现在？不，是拍完胶卷最后一张那一时刻。这个倒计时还在继续吗？',\n",
       " '汪淼走出暗室，取出一只新的黑白胶卷装到莱卡相机上，在房间里飞快地随意拍摄起来，最后又到阳台上拍了几张室外的画面。胶卷拍完后，他把它从相机里取出来，一头钻进暗室冲洗。冲出来的胶片上，那数字幽灵般地在每一张底片上不断显示出来，第一张是1187:27:39，从上一卷最后一张拍摄到拍这卷的第一张，正好是间隔这么长时间。以后的每一张的计时间隔为三到四秒，1187:27:35、1187:27:31、1187:27:27、1187:27:24……是他快速拍摄的间隔。',\n",
       " '倒计时仍在继续。',\n",
       " '汪淼再次给相机装上新胶卷，飞快地乱拍起来，有几张他是故意扣上镜头盖拍的。当他将拍完的胶卷取出时，妻子和孩子回来了。在去冲洗前，他给莱卡装上第三个胶卷，把相机递给妻子：“来，拍完这卷。”',\n",
       " '“拍什么？”妻子惊诧地看着丈夫。以前，他是绝不允许其他人碰自己的相机，当然她和儿子对那玩意儿也没兴趣，在他们眼里，那是一个两万多元买来的乏味的老古董。',\n",
       " '“什么都行，随便拍。”汪淼把相机塞到妻子手中，一头钻进了暗室。',\n",
       " '“那，豆豆，我给你拍吧。”妻子把镜头对准了儿子。',\n",
       " '汪淼的脑海中突然浮现出幽灵般的数字像一条张开的绞索横在孩子面容前的幻象，他不由微微战栗了一下。“不，别拍儿子，随便拍别的什么吧。”',\n",
       " '快门“咔嚓”一声，妻子拍了第一张，然后叫道：“这怎么按不动了？”汪淼教妻子扳了一个手柄，“这样，每次都要倒卷。”然后钻进了暗室。',\n",
       " '“真麻烦。”身为医生的妻子不能理解，在千万级像素的数码相机已经普及的今天，还有人用这种过时的昂贵玩意儿，而且拍的还是黑白胶卷。',\n",
       " '胶卷冲出来后，对着晕暗的红灯，汪淼看到那幽灵倒计时仍在继续，在一张张随意拍出的混乱画面上，包括那几张扣着镜头盖拍的，清晰地显示出：1187:19:06、1187:19:03、1187:18:59、1187:18:56……',\n",
       " '妻子敲了两下暗室的门，告诉他拍完了。汪淼出门抓过相机，取胶卷时他的手明显地在颤抖。不顾妻子异样的目光，他拿着胶卷又回到暗室，死死地关上门。他干得很忙乱，显影液、定影液洒了一地，胶卷很快冲出来了，他闭上双眼，默默祈祷：别出现，不管是什么，别在现在出现，别轮到我……',\n",
       " '他用放大镜沿着湿漉漉的胶卷看去，倒计时消失了，底片上只有妻子拍出的室内画面，在低速光圈下，她那不专业的操作拍出的画面一片模糊，但汪淼觉得这是他看过的最赏心悦目的照片了。',\n",
       " '汪淼走出暗室，长出一口气，发现汗水已浸湿了全身。妻子去厨房做饭了，儿子也到自己的房间去玩，他一个人坐在沙发上，开始了稍微冷静的思考。',\n",
       " '首先，这组在不同的拍摄间隔精确地记录时间流逝，并显示出智能迹象的数字，不可能是预留在胶片上的，只能是某种力量使其感光，那会是什么呢？是相机的问题吗？是某种装置被有意无意地放置到了相机中吗？他将镜头卸下来，把相机拆开，用放大镜仔细地观察着相机内部，检查着每个一尘不染的光洁机件，没有发现任何异常。那么，联想到那几张扣上镜头盖后拍摄的画面，最可能的感光源是外界某种穿透力很强的射线，但这在技术上同样是不可能的：射线源在哪儿？如何瞄准？',\n",
       " '至少以现有技术而言，这种力量是超自然的。',\n",
       " '为了进一步确定幽灵倒计时已经消失，汪淼又在莱卡相机中装上了一个胶卷，开始一张张地随意拍起来。当这次的胶卷冲出来后，刚刚稍微平静了一会儿的他又被推到了疯狂的边缘：幽灵倒计时又出现了，从画面显示的时间看，它根本就没有停止过，只是在妻子拍的那卷上没有显示而已。',\n",
       " '1186:34:13、1186:34:02、1186:33:46、1186:33:35……',\n",
       " '汪淼冲出暗室，冲出家门，猛敲邻居的门，开门的是退休的张教授。',\n",
       " '“老张，你家有没有相机？哦，不要数码的，要用胶卷的！”',\n",
       " '“你这大摄影家朝我借相机？那个两万多的坏了？我只有数码的……你不舒服？脸色这么差。”',\n",
       " '“借我用用。”',\n",
       " '老张很快拿来一架很普通的柯达数码相机。“给，里面的几张删掉就行……”',\n",
       " '“谢谢！”汪淼抓过相机和胶卷，匆匆返回屋里。其实家里还有三架胶卷相机和一架数码相机，但汪淼觉得从别处借更可靠些。他看着摊放在沙发上的两架相机和几只黑白胶卷，略一思考后，又给莱卡装上了胶卷，然后将数码相机递给正在端饭的妻子：',\n",
       " '“快，拍几张，就像刚才一样！”',\n",
       " '“这是干什么？看你的脸色……你到底怎么了？！”妻子惊恐地望着他。',\n",
       " '“你别管，拍！”',\n",
       " '妻子放下手中的碟子，走过来看着丈夫，眼中的惊恐又加上了忧虑。',\n",
       " '汪淼把柯达相机塞到过来吃饭的六岁儿子手里，“豆豆，你帮爸爸拍。就按这个，对，这是一张；再按一下，对对，又是一张；就这样一直拍，对着哪儿都行。”',\n",
       " '儿子很快掌握了，小家伙很感兴趣，拍得很快。汪淼转身从沙发上拿起自己的莱卡，也拍了起来，父子俩就这样“咔嚓、咔嚓”地疯狂拍着，丢下妻子在频频闪光中不知所措，眼泪涌了出来。',\n",
       " '“汪淼，我知道你最近工作压力很大，你可别……”',\n",
       " '汪淼把莱卡相机的胶卷拍完，又从孩子手中抢过数码相机。他想了一下，为了避开妻儿的干扰，走到卧室中，自己用数码相机也拍了几张。他拍的时候用的是目视取景器，没用液晶屏，因为怕看到结果，虽然迟早要看。',\n",
       " '汪淼取出莱卡里的胶卷钻进暗室，紧紧地关上门工作起来。冲洗完成后，他细看底片，因手在颤抖，他只能用双手握着放大镜——底片上，幽灵倒计时在继续。',\n",
       " '汪淼冲出暗室，开始检查数码相机上的照片，从液晶屏上看到，刚才拍的数码照片中，儿子拍的部分没有显示倒计时；而在自己拍的那部分，倒计时清晰地显示出来，并且与底片上的同步变化。',\n",
       " '汪淼使用不同的相机拍摄，目的是排除问题出在相机或底片上的可能性，但他无意中让孩子拍摄，加上之前让妻子拍摄，得出了一个更加诡异的结果：用不同相机和不同胶卷拍摄，别人拍出的都正常，幽灵倒计时只会在他拍摄的照片上出现！',\n",
       " '   ',\n",
       " '汪淼绝望地抓起那堆胶卷，像抓着一团纠缠在一起的蛇，又像一团难以挣脱的绞索。',\n",
       " '他知道，仅凭自己的力量是无法解决这个问题的，那么去找谁呢？大学和研究所里的同事是不行的，他们与自己一样，都是技术型思维的人；直觉告诉他，这件事已超出了技术之外。他想到了丁仪，可现在这人自己也陷入精神危机之中。他最后想到了“科学边界”，那是一群思想深刻而且活跃的人。于是，他拨通了申玉菲的电话。',\n",
       " '“申博士，我这里有些事，必须到你那里去一趟。”汪淼急促地说。',\n",
       " '“来吧。”申玉菲只说了这两个字就挂断了电话。',\n",
       " '汪淼吃了一惊，申玉菲平时说话也十分精简，以至于“科学边界”的一些人戏称她为“女海明威”。但这次，她竟连是什么事都不问，汪淼不知该感到安慰还是更加不安。',\n",
       " '他将那团胶卷塞进一个提包，并带上那架数码相机，在妻子焦虑的目光中冲出家门。本来可以开车去的，但即使在这灯火灿烂的城市，他在路上也想有人陪伴，于是叫了出租车。',\n",
       " '※※※',\n",
       " '申玉菲住在新城铁线附近的一个高档别墅区，这里的灯光稀疏了许多，别墅群环绕着几个能垂钓的小人工湖，晚上有一种乡村的感觉。申玉菲显然很富有，但汪淼一直搞不清她的财产来源，她以前的研究职位和现在公司中的职位都挣不到这么多钱。不过她的别墅中并没有豪华享受的痕迹，那里是“科学边界”的一个聚会场所，其中的陈设很像一个带会议室的小图书馆。',\n",
       " '在客厅里，汪淼见到申玉菲的丈夫魏成。这个四十岁左右的男人，一副敦厚的知识分子模样，汪淼对他的了解仅限于其姓名，申玉菲介绍时也只说了这些。他似乎没有工作，成天待在家里，对“科学边界”的讨论不感兴趣，对家里频繁来往的学者们也习以为常。',\n",
       " '但他并非无所事事，显然在家研究着什么东西，整天沉浸在思考中，见到任何人都是心不在焉地打个招呼，然后回到楼上的房间里，他一天的大部分时间都待在那里。一次，汪淼在楼上无意中从半开的房门向里瞥了一眼，看到一个令人惊奇的东西：一台HP小型机。他不会看错的，因为这台设备与他工作的超导研究中心那台一样，黑灰色机箱，是四年前出品的RX8620。把这台价值上百万的设备放在家里似乎很奇怪，魏成每天一个人守着它到底在干什么？',\n",
       " '“玉菲在上面有点事，您稍等一会儿吧。”魏成说，然后走上楼。汪淼本打算等的，但实在坐不住，也跟着走上楼去，看到魏成正要进入他那个放着小型机的房间。他看到汪淼跟来似乎并不反感，指指对面的一个房间说：“哦，就在那个房间里，你去找她吧。”',\n",
       " '汪淼敲门，门没锁，开了一个缝，他看到申玉菲正坐在电脑前玩游戏，令汪淼惊奇的是她竟穿着一套“V装具”。这是目前在游戏玩家中很流行的玩意儿，由一个全视角显示头盔和一套感应服构成，感应服可以使玩家从肉体上感觉到游戏中的击打、刀刺和火烧，能产生出酷热和严寒，甚至还能逼真地模拟出身体暴露在风雪中的感觉。汪淼走到她后面，由于游戏是在头盔中以全视角方式显示的，在显示器上什么都看不到。这时，汪淼想起大史让他记网址和邮件地址的事，无意中扫了一眼显示器，那个游戏登录界面上的英文名很特别，他记住了。',\n",
       " '申玉菲摘下显示头盔，又脱下了感应服，戴上她那副在瘦削的脸上显得很大的眼镜，面无表情地对汪淼点点头，一个字都没说，等着他说话。汪淼拿出那团胶卷，开始讲述发生在自己身上的诡异事件。申玉菲注意听着，对那些胶片，只是拿起来大概扫了几眼，并没有细看——这令汪淼很震惊，现在他进一步确定申玉菲对此事并非完全不知情，这几乎令他停止了讲述，只是申玉菲几次点头示意他继续，才将事情讲完了。这时申玉菲才说出了他们见面后的第一句话：“你领导的纳米项目怎么样了？”',\n",
       " '这不着边际的问题令汪淼十分吃惊。“纳米项目？它与这有什么关系？”他指指那堆胶卷。',\n",
       " '申玉菲没有说话，只是静静地看着他，等他回答自己的问题。这就是她的谈话风格，从不多说一个字。',\n",
       " '“把研究停下来。”申玉菲说。',\n",
       " '“什么？”汪淼认为自己听错了，“你说什么？”',\n",
       " '申玉菲沉默着，没重复自己的话。',\n",
       " '“停下来？！那是国家重点项目！”',\n",
       " '申玉菲仍不说话，只是看着他，目光平静。',\n",
       " '“你总得说出原因吧！”',\n",
       " '“停下来试试。”',\n",
       " '“你到底知道些什么？告诉我！”',\n",
       " '“我能告诉你的就这些了。”',\n",
       " '“项目不能停，也不可能停！”',\n",
       " '“停下来试试。”',\n",
       " '关于幽灵倒计时的简短谈话就到此为止，之后，不管汪淼如何努力，申玉菲再也没有说出一个与此有关的字，只是重复那句话：“停下来试试。”',\n",
       " '“我现在明白了，‘科学边界’并不是像你们宣称的那样是一个基础理论的学术交流组织，它与现实的关系比我想象的要复杂得多。”汪淼说。',\n",
       " '“相反，你得出这个印象，是因为‘科学边界’涉及的东西比你想象的更基础。”',\n",
       " '绝望的汪淼没有告辞起身就走，申玉菲默默地一直送他到庭院的大门处，并看着他坐进出租车。正在这时，另一辆汽车疾驰而来，在门前刹住了。一个男人下车，借着别墅中透出的灯光，汪淼一眼就认出了他。',\n",
       " '这人是潘寒，是“科学边界”里最著名的人物之一。作为一名生物学家，他成功地预言了长期食用转基因农产品造成的后代遗传畸形，还预言了转基因作物可能造成的生态灾难。与那些空洞地危言耸听的学者不同，他的预言充满了具体的细节，且都一一精确兑现，其准确度达到令人震惊的程度，以至于有传言说他来自未来。',\n",
       " '他使自己闻名于世的另一个创举，是创建了国内第一个实验社会。与西方那些旨在回归自然的乌托邦社团不同，他的“中华田园”不是处于荒野之地，而是置身于最大的城市中。社团没有一分钱财产，包括食物在内的所有生活用品，均来自城市垃圾。与人们最初的预想不同，“中华田园”不但生存下来，而且迅速壮大，其固定成员已达三千多人，不定期到其中体验生活的人更是不计其数。',\n",
       " '以这两个成功为基础，潘寒的社会思想也日益具有影响力。他认为，科技革命是人类社会的一种病变，技术的爆炸性发展与癌细胞的飞速扩散相当，最终的结果都是耗尽有机体的养分，破坏器官，导致其寄宿体的死亡。他主张废除那些“粗暴的”技术，如化石能源和核电，保留“温和的”技术，如太阳能和小水电。将大城市逐步解散，人口均匀分布于自给自足的小村镇中，以“温和技术”为基础，建立“新农业社会”。',\n",
       " '“他在吗？”潘寒指指别墅的二楼问。',\n",
       " '申玉菲没有回答，沉默地挡在他面前。',\n",
       " '“我要警告他，当然也要警告你，别逼我们！”潘寒冷冷地说。',\n",
       " '申玉菲仍没回答他，只是对出租车里的汪淼说：“走吧，没事。”然后示意司机开车。车发动后，汪淼再也没有听到他们说什么，他回头远远地看到，灯光下申玉菲一直没让潘寒走进别墅。',\n",
       " '※※※',\n",
       " '回到家已是深夜，汪淼在小区的门口走下出租车，一辆黑色桑塔纳紧贴着他刹住，车窗摇下，一股烟喷了出来，是大史，粗壮的身躯将驾驶座挤得满满的。',\n",
       " '“哇，汪教授，汪院士！这两天过得可好？”',\n",
       " '“你在跟踪我？真无聊！”',\n",
       " '“别误会，我要是直直开过去不就完了，讲个礼貌打个招呼你还当成驴肝肺了。”大史露出他的特色傻笑，一副无赖相，“咋的，那边看到什么有用的信息没，交流交流？”',\n",
       " '“我说过，我和你们没关系了，今后请不要跟踪我！”',\n",
       " '“得——”大史开动了车子，“好像我愿意挣这俩夜班外勤费似的，球赛都耽误了。”',\n",
       " '※※※',\n",
       " '汪淼走进家门，妻儿已经睡了，他听到妻子在床上不安地翻身，嘴里发出模糊不清的声音，丈夫今天怪异的举动，不知会给她带来怎样的噩梦。汪淼吃了两片利眠灵，躺到床上，过了很长时间才艰难地进入梦乡。',\n",
       " '他的梦境很纷乱，但其中的一个东西却恒定地存在着：幽灵倒计时。其实，倒计时在梦中出现是汪淼早就预料到的事。梦境中，他疯狂地击打悬浮在半空的倒计时，撕它、咬它，但一切击打都无力地穿透了它，它就悬在梦境正中，坚定地流逝着。它使汪淼烦躁至极，终于从梦中醒来。',\n",
       " '他睁开眼，看到了模糊的天花板，外面城市的灯光透过窗帘，在上面投出黯淡的光晕。但有一样东西从梦中跟随他到现实中：幽灵倒计时。倒计时仍在他睁开的眼睛前显现，数字很细，但很亮，发出一种烧灼的白光。',\n",
       " '1185:11:34、1185:11:33、1185:11:32、1185:11:31……',\n",
       " '汪淼转转头，看到了卧室中模糊的一切，确认自己已经醒来，倒计时没有消失。他闭上双眼，倒计时仍显现在他那完全黑暗的视野中，像黑天鹅绒上发亮的水银。他再次睁眼，并揉揉眼睛，倒计时仍没有消失，不管他的视线如何移动，那一串数字稳稳地占据着视野的正中央。',\n",
       " '一股莫名的恐惧使汪淼猛地坐起来，倒计时死死跟随着他。他跳下床，冲到窗前，扯开窗帘，推开窗。外面沉睡中的城市仍然灯光灿烂，倒计时就在这广阔的背景前显现着，像电影画面上的字幕。',\n",
       " '一时间，汪淼感到自己窒息了，不由发出一声低沉的惊叫。面对被惊醒的妻子恐慌的探问，他努力使自己镇定下来，安慰妻子说没什么，又躺回床上，闭上眼睛，在幽灵倒计时的照耀下艰难地度过了剩下的夜晚。',\n",
       " '清晨起床后，汪淼努力使自己在家人面前显得正常些，但妻子还是看出了异样，问他的眼睛怎么了？是不是看不清东西？',\n",
       " '早饭后，汪淼向纳米中心请了假，开车去医院。一路上，幽灵倒计时无情地横在他眼中的现实世界前面，这东西会自动调节自己的亮度，在不同的背景上都清晰地显现出来。汪淼甚至盯着初升的太阳，试图使倒计时被强光暂时隐没一会儿，但没有用，那串魔鬼数字竟在日轮上显现出来，这时它不是增加亮度，而是变成黑色，更加恐怖。',\n",
       " '同仁医院很难挂号，汪淼直接找了妻子的一个同学，一位著名的眼科专家。他没有说病情，而是先让医生检查自己的眼睛。仔细检查了汪淼的双眼后，医生告诉他没有发现什么病变，眼睛一切正常。',\n",
       " '“我眼睛总是看见一个东西，不管你看哪里，这东西都在。”汪淼说。同时，那串数字就横在医生脸前。',\n",
       " '1175:11:34、1175:11:33、1175:11:32、1175:11:31……',\n",
       " '“飞蚊症。”医生说，同时抽出处方签开始写，“我们这年纪的常见眼病，晶状体混浊。不太好治，但没什么要紧的，开些碘药水和维D吧，也许能吸收掉，但希望不大。不过，这确实没什么要紧的，只要你习惯了忽略视野里的那些杂物，对视力没什么影响。”',\n",
       " '“你说的飞蚊症，那些……东西看上去是什么样子？”',\n",
       " '“不规则，因人而异，有时是小黑点儿，有时像蝌蚪。”',\n",
       " '“如果看到的是一串数字呢？”',\n",
       " '医生写处方的笔停了。',\n",
       " '“你看到一串数字？”',\n",
       " '“是的，横在视野中心。”',\n",
       " '医生推开纸和笔，关切地看着他，“一进来我就看出，你过度劳累。上次同学聚会，李瑶向我提起你，说你的工作压力很大。到我们这岁数，应该注意了，健康可透支不起了。”',\n",
       " '“你是说，我这是精神因素所致？”',\n",
       " '医生点点头，“要是一般的病人，我就建议他去精神科了，其实没必要，没什么要紧的，就是太累了。休息几天吧，去度几天假，和李瑶、孩子，叫什么来着，豆豆吧，一起去。放心，很快会恢复的。”',\n",
       " '1175:10:02、1175:10:01、1175:10:00，1175:09:59……',\n",
       " '“我告诉你我看到的是什么，一个倒计时！一秒一秒，在精确地走！这会是精神因素？”',\n",
       " '医生宽容地笑笑，“想知道精神因素能对视力影响到什么程度吗？上个月我们收治了一个女孩儿，十五六岁吧，她在教室里突然间什么都看不见了，完全失明。可经过所有检查，眼睛在生理上完全正常。后来精神科的专家对她进行了一个月的心理治疗，又是突然间，她的眼睛恢复到正常的视力水平。”',\n",
       " '汪淼知道在这里是浪费时间，他起身要走，最后说：“好吧，不管我的眼睛，我只有一个问题想请教你：有什么外力，能通过远程作用使人看到什么吗？”',\n",
       " '医生想了想说：“有，我前一阵儿参加神舟19号的医疗组，曾有航天员报告说，他们在舱外工作时看到了并不存在的闪光。以前国际空间站上的航天员报告过类似情况，都是在太阳活动剧烈的时候，太空中的高能粒子打到视网膜上，人就看到闪耀。不过你说的看到数字，还是倒计时，绝无可能是这个原因。”',\n",
       " '汪淼恍惚地走出医院，倒计时就在他眼前，他似乎在跟着它走，跟着一个死死缠着他的鬼魂。他买了一副墨镜戴上，仅仅是为了不让别人看到自己梦游般迷离的眼神。',\n",
       " '汪淼走进纳米中心的主体实验室，进门之前没忘记把墨镜摘下来，尽管这样，遇见他的同事都对他的精神状态露出担心的神色。',\n",
       " '在实验大厅中央，汪淼看到反应黑箱仍在运行中。这台巨型设备的主体是汇集了大量管道的一个球体。代号叫“飞刃”的超强度纳米材料已经生产出来，是用分子建筑术制造的，就是用分子探针  将材料分子像砌砖那样一个个垒砌起来，这样的工艺要耗费大量的资源，那些产品可以说是世界上最贵重的珍宝了，根本无法进行量产。',\n",
       " '实验室现在做的，就是试图通过一种催化反应来代替分子建筑法，使巨量的分子在反应中同时完成筑砌。试验就是在反应黑箱中进行的，这台设备可以在数量庞大的成分组合上进行反应试验，这样数量的组合如果用传统的人工操作可能上百年也做不完，但在反应黑箱中可以快速自动进行。同时，这是一种集现实反应与数字模拟一体化的设备，当合成进行到一定程度时，计算机会根据反应的阶段性结果建立起合成反应的数字模型，将剩下的反应进程用数字模拟代替，大大提高了实验效率。',\n",
       " '实验主任见到汪淼后，急匆匆走过来，开始汇报反应黑箱刚出现的一系列故障。这是近来汪淼一上班就遇到的事。现在，反应黑箱连续运行了一年多，许多传感器灵敏度下降，误差增大，急需停机维护。但身为项目首席科学家的汪淼坚持做完第三批合成组合再停机，工程师们只好在反应黑箱上加入越来越多的补偿修正装置，到现在这些装置本身也需要补偿修正，搞得整个项目组疲惫不堪。但主任小心翼翼地没提停机和暂停试验的事，怕汪淼又像上几次那样大发雷霆。他只是把困难都摆出来，意思也很明白。',\n",
       " '汪淼抬头看看反应黑箱，觉得它像一个子宫，工程师们正围着它忙碌，艰难地维持着正常的运行。在这场景前面，叠现着幽灵倒计时。',\n",
       " '1174:21:11、1174:21:10、1174:21:09、1174:21:08……',\n",
       " '停下来试试。汪淼脑海中突然响起申玉菲的话。',\n",
       " '“全面更新外围传感系统需要多长时间？”他问。',\n",
       " '“四五天吧，”实验主任突然看到了希望，赶紧加一句，“快些干，三天就行，汪总，我保证！”',\n",
       " '我并没有屈服，设备确实需要维修，因而试验必须暂停，与别的无关。汪淼在心里对自己说，然后转向主任，透过倒计时的数字看着他，“把试验停下来吧，停机维修，就照你说的时间表。”',\n",
       " '“好的汪总，我会很快给你一份更新方案，下午就能停机了！”主任兴奋地说。',\n",
       " '“现在就停吧。”',\n",
       " '主任像不认识似的看着汪淼，但旋即恢复了兴奋状态，好像生怕失掉这个机会似的。他拿起电话下了停机命令，项目组里那些疲惫的研究员和工程师一下子都兴奋起来，开始按程序扳动上百个复杂的开关，众多的监控屏一个接一个地黑了下来，最后，主监控屏上显示了停机状态。',\n",
       " '几乎与此同时，汪淼眼前的倒计时停止了走动，数字固定为1174:20:35。几秒钟后，数字闪动了几下，消失了。',\n",
       " '当没有幽灵倒计时覆盖的现实重现眼前时，汪淼长出了一口气，像刚从水底挣扎出来一样。他无力地坐下，很快意识到旁边还有人在看着他。',\n",
       " '他对实验主任说：“系统更新是设备部的事，你们实验组的人好好休息几天吧，这一阵大家都辛苦了。”',\n",
       " '“汪总，你也太累了，这里有张总工程师盯着，你也回家好好休息一下吧。”',\n",
       " '“是啊，太累了。”汪淼无力地说，待他离开后，拿起电话，拨了申玉菲的号码，只响了一声铃她就接了。',\n",
       " '“你们背后是什么？”汪淼问，尽量使自己的声音冷静一些，但没有做到。',\n",
       " '沉默。',\n",
       " '“倒计时的尽头是什么？”',\n",
       " '沉默。',\n",
       " '“你在听吗？”',\n",
       " '“在。”',\n",
       " '“高强度纳米材料怎么了？这不是高能加速器，只是一项应用研究，值得这样关注么？”',\n",
       " '“什么值得关注，不应由我们来判断。”',\n",
       " '“够了！”汪淼大吼一声，心中的恐惧和绝望突然化为疯狂的怒气，“你们以为这点小魔术就能骗得了我？就能阻止技术进步！？我承认一时无法做出技术上的解释，但那是因为我还没有绕到那个可耻魔术师的背后！”',\n",
       " '“你的意思，是想在更大的尺度上看到倒计时？”',\n",
       " '    ',\n",
       " '全息图像 ',\n",
       " '申玉菲的话让汪淼愣了一下，他对这个问题没有准备，于是强迫自己冷静下来，以免落入圈套。“收起你那套把戏吧。大尺度又怎么样，你们同样可以玩魔术！可以向天空投映全息图像  ，就像上一次战争中北约做的那样，强力激光甚至可以将图像映满整个月球表面！射手和农场主应该能够玩弄人类力不能及的更大尺度，比如，倒计时能够显示到太阳表面吗？”话刚说完，汪淼吃惊地张大了嘴，他竟在下意识中说出了那两个这时应十分忌讳的名词，还好，没有说出更忌讳的那个。他想争取更多的主动性，于是接着说，“考虑到某种我还没想到的可能性，即使在太阳的尺度上，你们那个可耻魔术师仍有可能耍魔术，那种力量要真正令人信服，显示的尺度还需更大些。”',\n",
       " '“问题是你能承受得了吗？我们是朋友，我想帮你，别走杨冬的路。”',\n",
       " '听到这个名字，汪淼不由打了个寒战，但随之而来的愤怒又使他不顾一切了：“能接受这个挑战吗？”',\n",
       " '“能。”',\n",
       " '“你想怎么样？”汪淼的声音变得无力了。',\n",
       " '“你旁边有上网的电脑吗？好，进这个网址：http://www.qsl.net/bg3tt/zl/mesdm.htm，打开了吗？把网页打印出来，随身带着。”',\n",
       " '汪淼看到网页上显示的只是一张莫尔斯电码对照表。',\n",
       " '“我不明白，这是……”',\n",
       " '“在以后的两天内，设法找到一个能够观测宇宙背景辐射的地方。具体的请看我随后发给你的电子邮件。”',\n",
       " '“这是……干什么呢？”',\n",
       " '“我知道纳米研究项目已经停了，你打算重新启动它吗？”',\n",
       " '“当然，三天以后。”',\n",
       " '“那倒计时将继续。”',\n",
       " '“我将在什么尺度上看到它？”',\n",
       " '沉默良久，这个为某种超出人类理解力的力量代言的女人，冷酷地封死了汪淼的一切出路。',\n",
       " '“三天后，也就是十四日，在凌晨一点钟至五点钟，整个宇宙将为你闪烁。”',\n",
       " '分子探针来源于扫描隧道显微镜，是一种利用量子理论中的隧道效应探测物质表面结构的仪器。它可以让科学家观测和定位单个原子，具有比它的同类原子力显微镜更加高的分辨率。此外它在低温（4K）可以利用探针尖端精确操纵原子，因此在纳米科技既是重要的测量工具又是加工工具。 ',\n",
       " '全息照相是美国科学家伯格（M.J. Buerger）在利用X射线拍摄晶体的原子结构照片时发现的，他与盖伯（Gabor）一起创建了全息照相理论，即利用双光束干涉原理，令物光和另一个与物光相干的光束（参考光束）产生干涉图样即可把位相“合并”上去，从而用感光底片同时记录下位相和振幅，就可以获得全息图像。但由于拍摄和再现时的特殊要求，全息技术从诞生之日起，就几乎一直被局限在实验室里。 ',\n",
       " '',\n",
       " '7.\\u3000三体、周文王、长夜 ',\n",
       " '汪淼拨通了丁仪的电话，对方接听后，他才想起现在已是凌晨一点多了。',\n",
       " '“我是汪淼，真对不起，这么晚打扰。”',\n",
       " '“没关系，我正失眠。”',\n",
       " '“我……遇到一些事，想请你帮个忙。你知道国内有观测宇宙背景辐射的机构吗？”汪淼产生了一种倾诉的欲望，但旋即觉得幽灵倒计时之事目前还是不要让更多的人知道为好。',\n",
       " '“宇宙背景辐射？你怎么对这个有雅兴？看来你真的遇到一些事了……你去看过杨冬的母亲吗？”',\n",
       " '“啊——真对不起，我忘了。”',\n",
       " '“没关系，现在科学界，很多人都……像你说的那样遇到了一些事，心不在焉的。不过你最好还是去看看她，她年纪大了，又不愿雇保姆，要是有什么费力气的事麻烦你帮着干干……哦，宇宙背景辐射的事，你正好可以去找杨冬的母亲问问，她退休前是搞天体物理专业的，与国内的这类研究机构很熟。”',\n",
       " '“好好，我今天下班就去。”',\n",
       " '“那先谢谢了，我是真的无法再面对与杨冬有关的一切了。”',\n",
       " '※※※',\n",
       " '打完电话后，汪淼坐到电脑前，开始打印网页上显示的那张很简单的莫尔斯电码对照表。这时他已经冷静下来，将思绪从倒计时上移开，想着关于“科学边界”和申玉菲的事，想到她玩的网络游戏。关于申玉菲，他能肯定的唯一一件事就是她不是爱玩游戏的人，这个说话如电报般精简的女人给他唯一的印象就是冷，她的冷与其他的某些女性不同，不是一张面具，而是从里到外冷透了。',\n",
       " '汪淼总是下意识地将她与早已消失的DOS操作系统联系在一起，一面空荡荡的黑屏幕，只有一个简单得不能再简单的“C:>”提示符在闪动，你输入什么它就输出什么，一个字都不会多，也不会有变化。现在他知道，“C:>”提示符后面其实是一个无底深渊。',\n",
       " '她真会有兴致玩游戏，而且是戴着V装具玩儿？她没有孩子，那套V装具只能是自己买回去用的，这有些不可思议。',\n",
       " '汪淼在浏览器的地址栏中输入那个很容易记住的游戏网址：www.threebody.com，网页上显示该游戏只支持V装具方式。汪淼想起了纳米中心的职工娱乐室里好像有一套V装具，就走出已经空荡荡的中心实验大厅，去值班室要了钥匙，在娱乐室中穿过一排台球桌和健身器材，在一台电脑旁找到了V装具，费了很大劲才把感应服穿上，然后戴上显示头盔，启动电脑。',\n",
       " '启动游戏后，汪淼置身于一片黎明之际的荒原，荒原呈暗褐色，细节看不清楚，远方地平线上有一小片白色的曙光，其余的天空则群星闪烁。一声巨响，两座发着红光的山峰砸落到远方的大地上，整个荒原笼罩在红色光芒之中。被激起的遮天蔽日的尘埃散去后，汪淼看清了那两个顶天立地的大字：三体。',\n",
       " '随后出现了一个注册界面，汪淼用“海人”这个ID注册，然后成功登录。',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '荒原依旧，但V装具感应服中的压缩机咝咝地启动了，汪淼感到一股逼人的寒气。前方出现了两个行走的人影，在曙光的背景前呈黑色的剪影。汪淼追了上去，他看到两人都是男性，披着破烂的长袍，外面还裹着一张肮脏的兽皮，都带着一把青铜时代那种又宽又短的剑，其中一人背着一只有他一半高的细长的木箱子。那人扭头看看汪淼，他的脸像那兽皮一样脏和皱，双眼却很有神，眸子映着曙光。“冷啊。”他说。 ',\n",
       " '“是，真冷。”汪淼附和道。 ',\n",
       " '“这是战国时代，我是周文王。”那人说。 ',\n",
       " '“周文王不是战国时代的人吧？”汪淼问。 ',\n",
       " '“他一直活到现在呢，纣王也活着。”另一个没背箱子的人说，“我是周文王的追随者，我的ID就叫‘周文王追随者’，他可是个天才。” ',\n",
       " '“我的ID是‘海人’，”汪淼说，“您背的是什么？” ',\n",
       " '周文王放下那只长方形木箱，将一个立面像一扇门似的打开，露出里面的五层方格，借着晨曦的微光，汪淼看到每层之间都有高低不等的一小堆细沙，每格中都有从上一格流下的一道涓细的沙流。 ',\n",
       " '“沙漏，八小时漏完一次，颠倒三次就是一天，不过我常常忘了颠倒，要靠追随者提醒。”周文王介绍说。 ',\n",
       " '“你们好像是在长途旅行，有必要背这么笨重的计时器吗？” ',\n",
       " '“那怎么计时呢？” ',\n",
       " '“拿个小型的日晷多方便，或者干脆只看太阳也能知道大概的时间。” ',\n",
       " '周文王和追随者面面相觑，然后一起盯着汪淼，好像他是个白痴，“太阳？看太阳怎么能知道时间？这可是乱纪元。” ',\n",
       " '汪淼正要询问这个怪异名词的含义，追随者哀鸣道：“真冷啊，冷死我了！” ',\n",
       " '汪淼也觉得冷，但他不能随便脱下感应服，一般情况下，那样做会被游戏注销ID。他说：“太阳出来就会暖和些的。” ',\n",
       " '“你在冒充伟大的先知吗？连周文王都不算先知呢！”追随者冲汪淼不屑地摇摇头。 ',\n",
       " '“这需要先知吗？谁还看不出来太阳一两个小时后就会升起。”汪淼指指天边说。 ',\n",
       " '“这是乱纪元！”追随者说。 ',\n",
       " '“什么是乱纪元？” ',\n",
       " '“除了恒纪元，都是乱纪元。”周文王说，像回答一个无知孩童的提问。 ',\n",
       " '果然，天边的晨光开始暗下去，很快消失了，夜幕重新笼罩了一切，苍穹星光灿烂。 ',\n",
       " '“原来现在是黄昏不是早晨？”汪淼问。 ',\n",
       " '“是早晨，早晨太阳不一定能升起，这是乱纪元。” ',\n",
       " '寒冷使汪淼很难受。“看这样子，太阳要很长时间以后才会升出来。”他哆嗦着指指模糊的地平线说。 ',\n",
       " '“你怎么又会有这种想法？那可不一定，这是乱纪元。”追随者说着转向周文王，“姬昌，给我些鱼干吃吧。” ',\n",
       " '“不行！”周文王断然说道，“我也是勉强吃饱，要保证我能走到朝歌，而不是你。” ',\n",
       " '说话间，汪淼注意到另一个方向的地平线又出现了曙光，他分不清东南西北，但肯定不是上次出现时的方向。这曙光很快增强，不一会儿，这个世界的太阳升起来了，是一颗蓝色的小太阳，很像增强了亮度的月亮，但还是让汪淼感到了一丝温暖，并看清了大地的细节。但这个白昼很短暂，太阳在地平线上方划了一道浅浅的弧形就落下了，夜色和寒冷又笼罩了一切。 ',\n",
       " '三人在一棵枯树前停下，周文王和追随者拔出青铜剑来砍柴，汪淼将碎柴收集到一块。追随者拿出火镰，噼啪、噼啪打了好一阵，升起了一堆火。汪淼的感应服的前胸部分变暖和了，但背后仍然冰冷。 ',\n",
       " '“烧些脱水者，火才旺呢。”追随者说。 ',\n",
       " '“住嘴！那是纣王干的事！” ',\n",
       " '“反正路上那些散落的，都破成那样，泡不活了。如果你的理论真能行，别说烧一些，吃一些都成，与那理论相比，几条命算什么。” ',\n",
       " '“胡说！我们是学者！” ',\n",
       " '篝火燃尽后，三人继续赶路。由于他们之间交谈很少，系统加快了游戏时间的流逝速度，周文王很快将背上的沙漏翻了六下，转眼间两天过去了，太阳还没有升起过一次，甚至天边连曙光的影子都没有。 ',\n",
       " '“看来太阳不会出来了。”汪淼说，同时调出游戏界面来看了一下自己的HP，它正因寒冷而迅速减小。 ',\n",
       " '“你又冒充伟大的先知了……”追随者说，汪淼和他一起说出了后半句，“这是乱纪元！” ',\n",
       " '这话说完不久，天边真的出现了曙光，并且迅速增强，转眼间太阳就升了起来。汪淼发现这次升起的是一颗大太阳，当它升至一半时，直径占了视野内至少五分之一的地平线。暖流扑面而来，令汪淼心旷神怡，但他看周文王和追随者时，发现他们都一脸惊恐，仿佛魔鬼降临。 ',\n",
       " '“快，找阴凉地儿！”追随者大喊，汪淼跟着他们飞奔，跑到了一处低矮的岩石后面蹲下来。岩石的阴影在渐渐缩短，周围的大地像处于白炽状态般刺眼，脚下的冻土迅速融化，由坚硬如铁变成泥泞一片，热浪滚滚。汪淼很快出汗了。当大太阳升到头顶正上方时，三人用兽皮蒙住头，强光仍如利箭般从所有缝隙和孔洞中射进来。三人绕着岩石挪到另一边，躲进那边刚刚出现的阴影中…… ',\n",
       " '太阳落山后，空气依然异常闷热，大汗淋漓的三人坐在岩石上，追随者沮丧地说：“乱纪元旅行，真是在地狱里走路，我受不了了；再说我也没吃的了，你不分我些鱼干，又不让吃脱水者，唉——” ',\n",
       " '“那你只能脱水了。”周文王说，一手用兽皮扇着风。 ',\n",
       " '“脱水以后，你不会扔下我吧？” ',\n",
       " '“当然不会，我保证把你带到朝歌。” ',\n",
       " '追随者脱下了被汗水浸湿的长袍，赤身躺到泥地上。在落日的余晖中，汪淼看到追随者身上的汗水突然增加了，他很快知道那不是出汗，这人身体内的水分正在被彻底排出，这些水在沙地上形成了几条小小的溪流，追随者的整个躯体如一根熔化的蜡烛在变软变薄……十分钟后水排完了，那躯体化为一张人形的软皮一动不动地铺在泥地上，面部的五官都模糊不清了。 ',\n",
       " '“他死了吗？”汪淼问。他想起来了，一路上不时看到有这样的人形软皮，有的已破损不全，那就是不久前追随者想要用来烧火的脱水者。 ',\n",
       " '“没有。”周文王说着，将追随者变成的软皮拎起来，拍了拍上面的土，放到岩石上将他（它）卷起来，就像卷一只放了气的皮球一般，“在水里泡一会儿，他就会恢复原状活过来，就像泡干蘑菇那样。” ',\n",
       " '“他的骨骼也变软了？” ',\n",
       " '“是的，都成了干纤维，这样便于携带。” ',\n",
       " '“这个世界中的每个人都能脱水吗？” ',\n",
       " '“当然，你也能，要不，在乱纪元是活不下去的。”周文王将卷好的追随者递给汪淼，“你带着他吧，扔到路上不是被人烧了，就是吃了。” ',\n",
       " '汪淼接过软皮，很轻的一小卷，用胳膊夹着倒也没有什么异样的感觉。 ',\n",
       " '汪淼夹着脱水的追随者，周文王背着沙漏，两人继续着艰难的旅程。同前几天一样，这个世界中的太阳运行得完全没有规律，在连续几个严寒的长夜后，可能会突然出现一个酷热的白天，或者相反。两人相依为命，在篝火边抵御严寒，泡在湖水中度过酷热。好在游戏时间可以加快，一个月可以在半小时内过完，这使得乱纪元的旅程还是可以忍受的。 ',\n",
       " '这天，漫漫长夜已延续了近一个星期（按沙漏计时），周文王突然指着夜空欢呼起来： ',\n",
       " '“飞星！飞星！两颗飞星！！” ',\n",
       " '其实，汪淼之前就注意到那种奇怪的天体，它比星星大，能显出乒乓球大小的圆盘形状，运行速度很快，肉眼能明显地看到它在星空中移动，只是这次出现了两个。 ',\n",
       " '周文王解释说：“两颗飞星出现，恒纪元就要开始了！” ',\n",
       " '“以前看到过的。” ',\n",
       " '“那只有一个。” ',\n",
       " '“最多只有两个吗？” ',\n",
       " '“不，有时会有三个，但不会再多了。” ',\n",
       " '“三颗飞星出现，是不是预示着更美好的纪元？” ',\n",
       " '周文王用充满恐惧的眼神瞪了汪淼一眼，“你在说什么呀，三颗飞星……祈祷它不要出现吧。” ',\n",
       " '周文王的话没错，他们向往的恒纪元很快开始了，太阳升起落下开始变得有规律，一个昼夜渐渐固定在十八小时左右，日夜有规律的交替使天气变得暖和了一些。 ',\n",
       " '“恒纪元能持续多长时间？”汪淼问。 ',\n",
       " '“一天或一个世纪，每次多长谁都说不准。”周文王坐在沙漏上，仰头看着正午的太阳，“据记载，西周曾有过长达两个世纪的恒纪元，唉，生在那个时代的人有福啊。” ',\n",
       " '“那乱纪元会持续多长时间呢？” ',\n",
       " '“不是说过嘛，除了恒纪元都是乱纪元，两者互为对方的间隙。” ',\n",
       " '“那就是说，这是一个全无规律的混乱世界？！” ',\n",
       " '“是的，文明只能在较长的气候温暖的恒纪元里发展。大部分时间里，人类集体脱水存贮起来，当较长的恒纪元到来时，再集体浸泡复活，生产和建设。” ',\n",
       " '“那怎样预知每个恒纪元到来的时间和长短呢？” ',\n",
       " '“做不到，从来没有做到过。当恒纪元到来时，国家是否浸泡取决于大王的直觉，常常是：浸泡复活了，庄稼种下了，城镇开始修筑，生活刚刚开始，恒纪元就结束了，严寒和酷热就毁灭了一切。”周文王说到这里，一手指向汪淼，双眼变得炯炯有神，“好了，你已经知道了这个游戏的目标：就是运用我们的智力和悟性，分析研究各种现象，掌握太阳运行的规律，文明的生存就维系于此。” ',\n",
       " '“在我看来太阳运行根本就没有规律。” ',\n",
       " '“那是因为你没能悟出世界的本原。” ',\n",
       " '“你悟出来了？” ',\n",
       " '“是的，这就是我去朝歌的目的，我将为纣王献上一份精确的万年历。” ',\n",
       " '“可这一路上，没看到你有这种能力。” ',\n",
       " '“对太阳运行规律的预测只能在朝歌做出，因为那里是阴阳的交汇点，只有在那里取的卦才是准确的。”两人又在严酷的乱纪元跋涉了很长时间，其间又经历了一次短暂的恒纪元，终于到达了朝歌。 ',\n",
       " '汪淼听到一种不间断的类似于雷声的轰鸣。这声音是朝歌大地上许多奇怪的东西发出的，那是一座座巨大的单摆，每座都有几十米高。单摆的摆锤是一块块巨石，被一大束绳索吊在架于两座细高石塔间的天桥上。每座单摆都在摆动中。驱动它们的是一群群身穿盔甲的士兵，他们合着奇怪的号子，齐力拉动系在巨石摆锤上的绳索，维持着它的摆动。汪淼发现，所有巨摆的摆动都是同步的，远远看去，这景象怪异得使人着迷，像大地上竖立着一座座走动的钟表，又像从天而降的许多巨大、抽象的符号。 ',\n",
       " '   ',\n",
       " '在巨摆的环绕下，有一座巨大的金字塔，夜幕中如同一座高耸的黑山，这就是纣王的宫殿。汪淼跟着周文王走进了金字塔基座上的一个不高的洞门，门旁几名守卫的士兵在黑暗中如幽灵般无声地徘徊。他们沿着一条长长的隧道向里走，隧道窄而黑，间隔很远才有一枝火炬。 ',\n",
       " '“在乱纪元，整个国家在脱水中，但纣王一直醒着，陪伴着这片没有生机的国土。要想在乱纪元生存，就得居住在这种墙壁极厚的建筑中，几乎像住在地下，才能避开严寒和酷热。”周文王边走边对汪淼解释。 ',\n",
       " '走了很长的路，才进入了纣王位于金字塔中心的大殿，其实这里并不大，很像一个山洞。身披一大张花兽皮坐在一处高台上的人显然是纣王了，但首先吸引汪淼目光的是一位黑衣人，他的黑衣几乎与大殿中浓重的阴影融为一体，那张苍白的脸仿佛是浮在虚空中。 ',\n",
       " '“这是伏羲。”纣王对刚进来的周文王和汪淼介绍那位黑衣人，仿佛他们一直就在那儿似的，而黑衣人才是新来的，“他认为，太阳是脾气乖戾的大神，他醒着的时候喜怒无常，是乱纪元；睡着时呼吸均匀，是恒纪元。伏羲建议竖起了外面的那些大摆，日夜不停地摆动，声称这对太阳神有强烈的催眠作用，能使其陷入漫长的昏睡。但直到现在，我们看到太阳神仍醒着，最多只是不时打打盹儿。” ',\n",
       " '纣王挥了一下手，有人端来一个陶罐，放到伏羲面前的小石台上——汪淼后来知道，那是一罐调味料。伏羲长叹一声，端起陶罐喝下去，那咕咚咕咚的声音仿佛黑暗深处有一颗硕大的心脏在跳动。喝了一半后，他将剩下的调味料倒在身上，然后扔下陶罐，走向大殿角落的一口架在火上的青铜大鼎，爬上鼎沿；他跳进大鼎，激起了一大团蒸气。 ',\n",
       " '“姬昌坐下，一会儿就开宴。”纣王指指那口大鼎说。 ',\n",
       " '“愚蠢的巫术。”周文王朝大鼎偏了下头，轻蔑地说。 ',\n",
       " '“你对太阳悟出了什么？”纣王问，火光在他的双眸中跳动。 ',\n",
       " '“太阳不是大神，太阳是阳，黑夜是阴，世界是在阴阳平衡中运转的，这不在我们的控制之中，但可以预测。”周文王说着，抽出青铜剑，在火炬照到的地板上画出了一对大大的阴阳鱼，然后以令人目眩的速度在周围画出了六十四卦，看上去如同火光中时隐时现的大年轮，“大王，这就是宇宙的密码，借助它，我将为您的王朝献上一部精确的万年历。” ',\n",
       " '“姬昌啊，我现在急需知道的，是下一个长恒纪元什么时候到来。” ',\n",
       " '“我将立刻为您占卜。”周文王说着，走到阴阳鱼中央盘腿坐下，抬头望着大殿的顶部，目光仿佛穿透了厚厚的金字塔看到了星空，他的双手手指同时在进行着复杂的运动，组合成一部高速运转的计算器。寂静中，只有大鼎中的汤发出咕嘟咕嘟的声响，仿佛煮在汤中的巫师在梦呓。 ',\n",
       " '周文王从阴阳图中站起来，头仍仰着，说：“下面将是一段为期四十一天的乱纪元，然后将出现为期五天的恒纪元，接下来是为期二十三天的乱纪元和为期十八天的恒纪元，然后是为期八天的乱纪元，当这段乱纪元结束后，大王，您所期待的长恒纪元就到来了，这个恒纪元将持续三年零九个月，其间气候温暖，是一个黄金纪元。” ',\n",
       " '“我们首先需要证实一下你前面的预测。”纣王不动声色地说。 ',\n",
       " '汪淼听到上方传来一阵轰隆隆的声音，大殿顶上的一块石板滑开，露出一处正方形的洞口，汪淼调整方向，看到这个方洞通到金字塔的外面，在这个方洞的尽头，汪淼看到了几颗闪烁的星星。 ',\n",
       " '游戏的时间加快了，由两名士兵看守的周文王带来的沙漏几秒钟就翻动一次，标志着八小时的流逝。上方的窗口无规律地闪烁起来，不时有一束乱纪元的阳光射进大殿，有时很微弱，如月光一般；有时则十分强烈，投在地上的方形光斑白炽明亮，使所有的火炬黯然失色。汪淼数着沙漏翻动的次数，当翻到一百二十次左右时，阳光投进窗口的间隔变得规则了，预测中的第一个恒纪元到来。沙漏再翻动十五下后，窗口的闪烁又紊乱起来，乱纪元又开始了。然后又是恒纪元，然后又是乱纪元，它们的开始和持续时间虽然有些小误差，但与周文王的预测已是相当的吻合了。当最后一段为期八天的乱纪元结束后，他预言的长恒纪元开始了。汪淼数着沙漏的翻动，二十天过去了，射进大殿的日光仍遵循着精确的节奏。这时，游戏时间的流逝被调整到正常。 ',\n",
       " '纣王向周文王点点头：“姬昌啊，我将为你树起一座丰碑，比这座宫殿还要高大。” ',\n",
       " '周文王深鞠一躬：“我的大王，让您的王朝苏醒吧，繁荣吧！” ',\n",
       " '纣王在石台上站起身，张开双臂，仿佛要拥抱整个世界，他用一种很奇怪的歌唱般的音调喊道：“浸泡——” ',\n",
       " '听到这号令，大殿内的人都跑向洞门。在周文王的示意下，汪淼跟着他沿着长长的隧道向金字塔外走去。走出洞门，汪淼看到时值正午，太阳在当空静静地照耀着大地，微风吹过，他似乎嗅到了春天的气息。周文王和汪淼一同来到了距金字塔不远的一处湖畔，湖面上的冰已融化了，阳光在微波间跳动。 ',\n",
       " '先出来的一队士兵高呼着：“浸泡！浸泡！”都奔向湖边一处形似谷仓的高大石砌建筑。在来的路上，汪淼不时在远处看到过这种建筑，周文王告诉他那是“干仓”，是存贮脱水人的大型仓库。士兵们打开干仓的石门，从中搬出一卷卷落满灰尘的皮卷，他们每人都抱着、夹着好几个皮卷，走向湖边，将那些皮卷扔进湖中。那些皮卷一遇到水，立刻舒展开来，一时间，湖面上漂浮着一片似乎是剪出来的薄薄的人形。每一张“人片”都在迅速吸水膨胀，渐渐地，湖面上的“人片”都变成了圆润的肉体，这些肉体很快具有了生命的迹象，一个个挣扎着从齐腰深的湖水中站立起来。他们睁大如梦初醒的眼睛看着这风和日丽的世界。“浸泡！”一个人高呼起来，立刻引来了一片欢呼声：“浸泡！浸泡！！”……这些人从湖中跑上岸，赤身裸体地奔向干仓，将更多的皮卷投入湖中，浸泡复活的人一群群从湖中跑出来。这一幕也发生在更远处的湖泊和池塘中，整个世界在复活。 ',\n",
       " '“噢，天啊！我的指头——” ',\n",
       " '汪淼顺着声音看去，见一个刚浸泡复活的人站在湖中，举着一只手哭喊道，那手缺了中指，血从手上断指处滴到湖中。其他复活者纷纷拥过他的身边，兴高采烈地奔向湖岸，没有人注意他。 ',\n",
       " '“行了，你就知足吧！”一个经过的复活者说，“有人整条胳膊腿都没了，有人脑袋被咬了个洞，如果再不浸泡，我们怕是都要被乱纪元的老鼠啃光了！” ',\n",
       " '“我们脱水多长时间了？”另一位复活者问。 ',\n",
       " '“看看大王宫殿上积的沙尘有多厚就知道了，刚听说现在的大王已不是脱水前的大王了，不知是他的儿子还是孙子。” ',\n",
       " '浸泡持续了八天才完全结束，这时所有的脱水人都已复活，世界又一次获得了新生。这八天中，人们享受着每天二十个小时、周期准确的日出日落。沐浴在春天的气息里，所有人都衷心地赞美太阳、赞美掌管宇宙的诸神。第八天夜里，大地上的篝火比天上的星星都密，在漫长的乱纪元中荒废的城镇又充满了灯火和喧闹，同文明以前的无数次浸泡一样，所有人将彻夜狂欢，迎接日出后的新生活。 ',\n",
       " '但太阳再也没有升起来。 ',\n",
       " '各种计时器都表明日出的时间已过，但各个方向的地平线都仍是漆黑一片。又过了十个小时，没有太阳的影子，连最微弱的晨光都见不到。一天过去了，无边的夜在继续着；两天过去了，寒冷像一只巨掌在暗夜中压向大地。 ',\n",
       " '“请大王相信我，这只是暂时的，我看到了宇宙中的阳在聚集，太阳就要升起来了，恒纪元和春天将继续！” ',\n",
       " '金字塔的大殿里，周文王跪在纣王端坐的石台下哀求道。 ',\n",
       " '“还是把鼎烧上吧。”纣王叹了口气说。 ',\n",
       " '“大王！大王！”一名大臣从洞门里跌跌撞撞地跑进来，带着哭腔喊道，“天上，天上有三颗飞星！！” ',\n",
       " '大殿中的所有人都惊呆了，空气仿佛凝固了，只有纣王仍然不动声色。他转向以前一直不屑于搭理的汪淼，“你还不知道出现三颗飞星意味着什么吧？姬昌啊，告诉他。” ',\n",
       " '“这意味着漫长的严寒岁月，冷得能把石头冻成粉末。”周文王长叹一声，说。 ',\n",
       " '“脱水——”纣王又用那歌唱般的声音喊道。其实，在外面的大地上，人们早已开始陆续脱水，重新变成人干以度过漫漫长夜，他们中的幸运者被重新搬入干仓，还有大量的人干被丢弃在旷野上。周文王慢慢站起身，朝架在火上的青铜大鼎走去，他爬上鼎沿，跳进去前停了几秒钟，也许是看到伏羲煮得烂熟的脸正在汤中冲他轻笑。 ',\n",
       " '“用文火。”纣王无力地说，然后转向其他人，“该EXIT的就EXIT吧，游戏到这儿已经没什么玩头了。” ',\n",
       " '洞门上方出现了发着红光的EXIT标志，人们纷纷向那里走去。汪淼也跟随而去，穿过洞门和长长的隧道来到了金字塔外，看到黑夜里大雪纷飞，刺骨的寒冷使他打了个冷战。天空的一角显示出游戏的时间又加快了。 ',\n",
       " '十天后，雪仍在下着，但雪片大而厚重，像是凝结的黑暗。有人在汪淼耳边低声说：“这是在下二氧化碳干冰了。”汪淼扭头一看，是周文王的追随者。 ',\n",
       " '又过了十天，雪还在下，但雪花已变得薄而透明，在金字塔洞门透出的火炬的微光中呈现出一种超脱的淡蓝色，像无数飞舞的云母片。 ',\n",
       " '“这雪花已经是凝固的氧、氮了，大气层正在绝对零度中消失。” ',\n",
       " '金字塔被雪埋了起来，最下层是水的雪，中层是干冰的雪，上层是固态氧、氮的雪。夜空变得异常晴朗，群星像一片银色的火焰。一行字在星空的背景上出现： ',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '这一夜持续了四十八年，第137号文明在严寒中毁灭了，该文明进化至战国层次。',\n",
       " '文明的种子仍在，它将重新启动，再次开始在三体世界中命运莫测的进化，欢迎您再次登录。',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '退出前，汪淼最后注意到的是夜空中的三颗飞星，它们相距很近，相互围绕着，在太空深渊中跳着某种诡异的舞蹈。 ',\n",
       " '',\n",
       " '8.\\u3000叶文洁 ',\n",
       " '汪淼摘下V装具后，发现自己的内衣已被冷汗浸透了，很像是从一场寒冷的噩梦中醒来。他走出纳米中心，下楼开车，按丁仪给的地址去杨冬的母亲家。',\n",
       " '乱纪元，乱纪元，乱纪元……',\n",
       " '这个概念在汪淼的头脑中萦绕。为什么那个世界的太阳运行会没有规律？一颗恒星的行星，不管其运行轨道是正圆还是偏长的椭圆，其围绕恒星的运动一定是周期性的，全无规律的运行是不可能的……汪淼突然对自己很恼火，他使劲地摇头想赶走头脑中的这一切，不过是个游戏嘛，但他失败了。',\n",
       " '乱纪元，乱纪元，乱纪元……',\n",
       " '见鬼！别去想它！！为什么非想它不可？为什么？！',\n",
       " '很快，汪淼找到了答案。他已经有很多年没有玩过电子游戏了，这些年来电子游戏的软硬件技术显然已经提高了很多，其中的虚拟现实场景和附加效果都是他学生时代所无法比拟的。但汪淼明白，《三体》的真实不在于此。记得在大三的一次信息课中，教授挂出了两幅大图片，一幅是画面庞杂精细的《清明上河图》，另一幅是一张空旷的天空照片，空荡荡的蓝天上只有一缕似有似无的白云。教授问这两幅画中哪一幅所包含的信息量更大，答案是后者要比前者大一至两个数量级！',\n",
       " '《三体》正是这样，它的海量信息是隐藏在深处的，汪淼能感觉到，但说不清。他突然悟出，《三体》的不寻常在于，与其他的游戏相比，它的设计者是反其道而行之——一般游戏的设计者都是尽可能地增加显示的信息量，以产生真实感；但《三体》的设计者却是在极力压缩信息量，以隐藏某种巨大的真实，就像那张看似空旷的天空照片。',\n",
       " '汪淼放松了思想的缰绳，任其回到《三体》世界。',\n",
       " '飞星！关键在于不引人注意的飞星，一颗飞星，二颗飞星，三颗飞星……这分别意味着什么？',\n",
       " '正想着，车已开到他要去的小区大门了。',\n",
       " '※※※',\n",
       " '在要去的那栋楼门口，汪淼看到一位六十岁左右的头发花白、身材瘦削的女性，戴着眼镜，提着一个大菜篮子吃力地上楼梯。他猜她大概就是自己要找的人，一问，她果然就是杨冬的母亲，叶文洁。听汪淼说明来意后，她露出发自内心的感动，她是汪淼常见到的那种老知识分子，岁月的风霜已消去了他们性情中所有的刚硬和火热，只剩下如水的柔和。',\n",
       " '汪淼拿过菜篮子同她一起上了楼，走进她的家门后发现，这里并不像他想象的那么冷清——有三个孩子在玩耍，最大的不超过五岁，小的刚会走路。杨母告诉汪淼，这都是邻居的孩子。',\n",
       " '“他们喜欢在我这儿玩儿，今天是星期天，他们的父母要加班，就把他们丢给我了……哦，楠楠，你的画儿画完了吗？嗯，真好看，起个题目吧！太阳下的小鸭子，好，奶奶给你题上，再写上六月九日，楠楠作……中午你们都想吃什么呢？洋洋？烧茄子？好好；楠楠？昨天吃过的荷兰豆？好好；你呢，咪咪？肉肉？不，你妈妈说了，不要吃那么多肉肉，不好消化的，吃鱼鱼好吗？看奶奶买回来的这么大的鱼鱼……”',\n",
       " '她肯定想要孙子或孙女，但即使杨冬活着，会要孩子吗？看着杨母和孩子们投入地对话，汪淼心想。',\n",
       " '杨母将篮子提进厨房，出来后对汪淼说：“小汪啊，我先去把菜泡上，现在的蔬菜农药残留很多，给孩子们吃至少要泡两小时以上……你可以先到冬冬的房间里看看。”',\n",
       " '杨母最后一句看似无意的提议令汪淼陷入紧张和不安之中，她显然看出了汪淼此行在内心深处的真正目的。她说完就转身回到厨房，没有看汪淼一眼，自然看不到他的窘态，她这几乎天衣无缝的善解人意令汪淼一阵感动。汪淼转身穿过快乐的孩子们，走向杨母刚才指向的那个房间。他在门前停住了，突然被一种奇异的感觉所淹没，仿佛回到了少年多梦的时节，一些如清晨露珠般晶莹脆弱的感受从记忆的深处中浮起，这里面有最初的伤感和刺痛，但都是玫瑰色的。',\n",
       " '汪淼轻轻推开门，扑面而来的淡淡的气息是他没有想到的，那是森林的气息，他仿佛进入了一间护林人的林间小屋。墙壁被一条条棕色的树皮覆盖着，三只凳子是古朴的树桩，写字台也是由三个较大的树桩拼成的，还有那张床，铺的显然是东北的乌拉草。这一切都很粗糙、很随意，没有刻意表现出某种美感。以杨冬的职位，她的收入是很高的，可以在任何一处高档社区买下房子，可她一直同母亲住在这里。',\n",
       " '汪淼走到树桩写字台前，上面的陈设很简单，没有与学术有关的东西，也没有与女性有关的东西；也许都已经拿走了，也许从来就没在这里存在过。他首先注意到一张镶在木镜框中的黑白照片，是杨冬母女的合影，照片中的杨冬正值幼年，母亲蹲下正好同她一样高。风很大，将两人的头发吹到一起。照片的背景很奇怪，天空呈网格状，汪淼仔细察看支撑那网络的粗大的钢铁结构，推想那是一个抛物面天线或类似的东西，因为巨大，它的边缘超出了镜头。',\n",
       " '照片中，小杨冬的大眼睛中透出一种令汪淼心颤的惶恐，仿佛照片外的世界令她恐惧似的。汪淼注意到的第二件东西是放在写字台一角的一本厚厚的大本子，首先令他迷惑的是本子的材质，他看到封面上有一行稚拙的字：“杨冬的huà（桦）皮本”。这才知道这本子是桦树皮做的，时光已经使银白色的桦皮变成暗黄。他伸手触了一下本子，犹豫了一下又缩了回来。',\n",
       " '“你看吧，那是冬冬小时候的画儿。”杨母在门口说。',\n",
       " '汪淼捧起桦皮本，轻轻地一页页翻看。每幅画上都有日期，明显是母亲为女儿注上的，就像他刚进门时看到的那样。汪淼又发现了一件多少让他不可理解的事：从画上的日期看，这时的杨冬已经三岁多了，这么大的孩子通常都能够画出比较分明的人或物体的形状；但杨冬的画仍然只是随意纷乱的线条，汪淼从中看出了一种强烈的恼怒和绝望，一种想表达某种东西又无能为力的恼怒和绝望，这种感觉，是这种年龄的普通孩子所不具有的。',\n",
       " '杨母缓缓地坐到床沿上，双眼失神地看着汪淼手中的桦皮本，她女儿就是在这里，在安睡中结束了自己的生命。汪淼在杨母身边坐下，他从来没有过如此强烈的愿望，要与他人分担痛苦。',\n",
       " '    ',\n",
       " '吴健雄 ',\n",
       " '当代最杰出的物理学家之一，在实验物理学研究上取得伟大的成就。她在实验室中首次证明了李政道和杨振宁关于弱相互作用中宇称不守恒的理论推测，推翻了宇称守恒定律。 ',\n",
       " '杨母从汪淼手中拿过桦皮本，抱在胸前，轻声说：“我对冬冬的教育有些不知深浅，让她太早接触了那些太抽象、太终极的东西。当她第一次表现出对那些抽象理论的兴趣时，我告诉她，那个世界，女人是很难进入的。她说居里夫人不是进入了吗？我告诉她，居里夫人根本没有进入，她的成功只是源于勤奋和执著，没有她，那些工作别人也会完成，倒是像吴健雄这样的女人还比她走得远些，但那真的不是女人的世界。女性的思维方式不同于男性，这没有高下之分，对世界来说都是必不可少的。',\n",
       " '“冬冬没有反驳我。到后来，我真的发现她身上有一些特殊的东西，比如给她讲一个公式，别的孩子会说‘这公式真巧妙’之类的，她则会说这公式真好看、真漂亮，那神情就像她看到一朵漂亮的野花一样。她父亲留下了一堆唱片，她听来听去，最后选择了一张巴赫的反复听，那是最不可能令孩子，特别是女孩子入迷的音乐了。开始我以为她是随意为之，但问她感受时，这孩子说：她看到一个巨人在大地上搭一座好大好复杂的房子，巨人一点一点地搭着，乐曲完了，大房子也就搭完了……”',\n",
       " '',\n",
       " '',\n",
       " '“您对女儿的教育真是成功。”汪淼感慨地说。',\n",
       " '“不，是失败啊！她的世界太单纯，只有那些空灵的理论。那些东西一崩溃，就没有什么能支撑她活下去了。”',\n",
       " '“叶老师，您这么想我觉得也不对，现在发生了一些让我们难以想象的事，这是一次空前的理论灾难，做出这种选择的科学家又不只是她一人。”',\n",
       " '“可只有她一个女人，女人应该像水一样的，什么样的地方都能淌得过去啊。”',\n",
       " '……',\n",
       " '告辞时，汪淼才想到了来访的另一个目的，于是他向杨母说起了观测宇宙背景辐射的事。',\n",
       " '“哦，这个，国内有两个地方正在做，一个在乌鲁木齐观测基地，好像是中科院空间环境观测中心的项目；另一个很近，就在北京近郊的射电天文观测基地，是中科院和北大那个联合天体物理中心搞的。前面那个是实际地面观察，北京这个只是接收卫星数据，不过数据更准确、全面一些。那里有我的一个学生，我帮你联系一下吧。”杨母说着，去找电话号码，然后给那个学生打电话，似乎很顺利。',\n",
       " '“没问题的，我给你个地址，你直接去就行。他叫沙瑞山，明天正好值夜班……你好像不是搞这专业的吧？”杨母放下电话问。',\n",
       " '“我搞纳米，我这是为了……另外一些事情。”汪淼很怕杨母追问下去，但她没有。',\n",
       " '“小汪啊，你脸色怎么这么不好？好像身体很虚。”杨母关切地问。',\n",
       " '“没什么，就是这样儿。”汪淼含糊地说。',\n",
       " '“你等等，”杨母从柜子里拿出一个小木盒，汪淼看到上面标明是人参，“过去在基地的一位老战士前两天来看我，带来这个……不，不，你拿去，人工种植的，不是什么珍贵的东西，我血压高，根本用不着的。你可以切成薄片泡茶喝，我看你脸色，好像血很亏的样子。年轻人一定要爱护自己啊。”',\n",
       " '汪淼的心中涌起一股暖流，双眼湿润了，他那颗两天来绷得紧紧的心脏像被放到了柔软的天鹅绒上。“叶老师，我会常来看您的。”他接过木盒说。',\n",
       " '',\n",
       " '9.\\u3000宇宙闪烁 ',\n",
       " '    ',\n",
       " '射电望远镜 ',\n",
       " '接收和研究天体无线电波（频率20kHz－3GHz）的天文观测装置，可以测量天体射电的强度、频率及偏振等量。 ',\n",
       " '汪淼驱车沿京密路到密云县，再转至黑龙潭，又走了一段盘山路，便到达中科院国家天文观测中心的射电天文观测基地。他看到二十八面直径为九米的抛物面天线在暮色中一字排开，像一排壮观的钢铁植物，2006年建成的两台高大的五十米口径射电望远镜天线矗立在这排九米天线的尽头，车驶近后，它们令汪淼不由想起了那张杨冬母女合影的背景。',\n",
       " '但叶文洁的学生从事的项目与这些射电望远镜没有什么关系，沙瑞山博士的实验室主要接收三颗卫星的观测数据：1989年11月升空、即将淘汰的微波背景探测卫星COBE，2001年发射的威尔金森微波各向异性探测卫星WMAP和2009年欧洲航天局发射的普朗克高精度宇宙微波背景探测卫星Planck。',\n",
       " '宇宙整体的微波背景辐射频谱非常精确地符合温度为2.726K的黑体辐射谱  ，具有高度各向同性  ，但在不同局部也存在大约百万分之五涨落的幅度。沙瑞山的工作就是根据卫星观测数据，重新绘制一幅更精确的全宇宙微波辐射背景图。这个实验室不大，主机房中挤满了卫星数据接收设备，有三台终端分别显示来自三颗卫星的数据。',\n",
       " '沙瑞山见到汪淼，立刻表现出了那种长期在寂寞之地工作的人见到来客的热情，问他想了解哪方面的观测数据。',\n",
       " '“我想观测宇宙背景辐射的整体波动。”',\n",
       " '“您能……说具体些吗？”沙瑞山看汪淼的眼神变得奇怪起来。',\n",
       " '“就是，宇宙3K微波背景辐射整体上的各向同性的波动，振幅在百分之一至百分之五之间。”',\n",
       " '沙瑞山笑笑，早在本世纪初，密云射电天文基地就对游客开放参观，为挣些外快，沙瑞山时常做些导游或讲座的事，这种笑容就是他回答游客（他已适应了那些骇人的科盲）问题时常常露出的。“汪先生，您……不是搞这个专业的吧？”',\n",
       " '“我搞纳米材料。”',\n",
       " '“哦，那就对了。不过，对于宇宙3K背景辐射，您大概有个了解吧？”',\n",
       " '“知道的不多。目前的宇宙起源理论认为，宇宙诞生于距今约一百四十亿年前的一次大爆炸。在诞生早期，宇宙温度极高，随后开始冷却，形成被称为微波背景辐射的‘余烬’。这种弥漫全宇宙的残留背景辐射，在厘米波段上是可以观测到的。好像是在一九六几年吧，两个美国人在调试一个高精度卫星接收天线时意外地发现了宇宙背景辐射……”',\n",
       " '“足够了，”沙瑞山挥手打断了汪淼的话，“那你就应该知道，与我们观测的不同部分的微小不均匀不同，宇宙整体辐射背景波动是随着宇宙的膨胀，在宇宙时间尺度上缓慢变化的，以Planck卫星的精度，直到一百万年后都未必能测出这种变化，你却想在今天晚上发现它百分之五的波动？！知道这意味着什么吗？这意味着整个宇宙像一个坏了的日光灯管那样闪烁！”',\n",
       " '而且是为我闪烁，汪淼心里说。',\n",
       " '“叶老师这是在开什么玩笑。”沙瑞山摇摇头说。',\n",
       " '“但愿真是个玩笑。”汪淼说，本想告诉他，叶文洁并不知道详情，但又怕因此招致他的拒绝，不过这倒是他的心里话。',\n",
       " '“既然是叶老师交代的，就观测吧，反正也不费劲，百分之一的精度用老古董COBE就行了。”沙瑞山说着，在终端上忙活起来，很快屏幕上出现一条平直的绿线，“你看，这就是当前宇宙整体背景辐射的实时数值曲线，哦，应该叫直线才对，数值是2.726±0.010K，那个误差是银河系运动产生的多普勒效应，已经滤掉了。如果发生你所说的超过百分之一振幅的波动，这条线就会变红并将波动显示出来。我敢打赌直到世界末日它也是条绿直线，要看到它显现肉眼看得到的变化，可能比看太阳毁灭还要等更长的时间。”',\n",
       " '“这不会影响您的正常工作吧？”',\n",
       " '“当然不会，那么粗的精度，用COBE观察数据的边角料就足够了。好了，从现在开始，如果那伟大的波动出现，数值会自动存盘。”',\n",
       " '“可能要等到凌晨一点。”',\n",
       " '“哇，这么精确？没关系，反正我本来就是值夜班。您吃饭了吗？那好，我带您去参观一下吧。”',\n",
       " '这一夜没有月亮，他们沿着长长的天线阵列漫步。沙瑞山指着天线说：“壮观吧？可惜都是聋子的耳朵。”',\n",
       " '“为什么？”',\n",
       " '    ',\n",
       " '米波综合孔径射电望远镜 ',\n",
       " '由英国赖尔发明，使射电望远镜实现成像观测，分辨率也能与光学望远镜并驾齐驱。 ',\n",
       " '“自它们建成以来在观测频段上就干扰不断，先是上世纪八十年代末的寻呼台，到现在是疯狂发展的移动通信。这些米波综合孔径射电望远镜能做的那些项目，像米波巡天、射电变源、超新星遗迹  研究等等，大部分都不能正常开展。多次找过无委会  ，没有用，我们能玩得过中国移动、联通、网通？没有钱，宇宙奥秘算个球！好在我的项目靠卫星数据，与这些‘旅游景观’无关了。”',\n",
       " '“近年来很多基础研究的商业运行还是很成功的，比如高能物理  。把观测基地建到离城市远些的地方应该好些吧？”',\n",
       " '“那还是钱的问题。就目前而言，只能是在技术上屏蔽干扰。唉，叶老师要在就好了，她在这方面造诣很深。”',\n",
       " '他们又谈了一会儿，沙瑞山问起进行这次奇怪观测的目的，汪淼避而不答，他也就没有再问。显然，一个专家的尊严，不允许他对这种违反专业常识的观测表现出过多的兴趣。',\n",
       " '然后他们来到一家为游客开的通宵酒吧。沙瑞山一杯接着一杯地灌啤酒，变得更加健谈。话题集中在叶文洁身上。',\n",
       " '他听沙瑞山讲她如何目睹父亲在“文革”中的惨死，讲她后来在建设兵团被诬陷，后来杳无音讯；九十年代初才又回到了这座城市，在父亲曾工作过的大学讲授天体物理学直到退休。',\n",
       " '“最近才知道，她那二十多年是在红岸基地度过的。”',\n",
       " '“红岸？！”汪淼被沙瑞山的讲述震撼了，好半天才对他最后一句话有了反应，“难道那些传说……”',\n",
       " '“大部分是真的。红岸自译解系统的一名研制者移民到欧洲，去年写了一本书，你所说的传说大多来自于那本书，据我了解是真的。红岸工程的参与者大都还健在。”',\n",
       " '“这可真是……传奇啊！”',\n",
       " '“尤其是发生在那个年代，更是传奇中的传奇。”',\n",
       " '……',\n",
       " '从她的学生那里，汪淼得知了她那历经风霜的前半生。而汪淼却早已心神不定，脑子里不断地浮现出那条绿色直线。直到差十分钟凌晨一点时，沙瑞山才接受了汪淼的多次提议，起身返回实验室。',\n",
       " '这时，照向射电天线阵列的聚光灯已经熄灭，天线在夜空下变成了简明的黑色二维图案，仿佛是一排抽象的符号，以同一个仰角齐齐地仰望着宇宙，似乎在等待着什么。这景象令汪淼不寒而栗，他想起了《三体》中的那些巨摆。',\n",
       " '回到实验室时正好是凌晨一点，当他们将目光投向终端屏幕时，波动刚刚出现，直线变成了曲线，出现了间隔不一的尖尖的波峰，颜色也变红了，如同一条冬眠后的蛇开始充血蠕动了。',\n",
       " '“肯定是COBE卫星的故障！”沙瑞山惊恐地盯着曲线说。',\n",
       " '“不是故障。”汪淼平静地说，在这样的事情面前，他已经初步学会了控制自己。',\n",
       " '   ',\n",
       " '“我们马上就能知道！”沙瑞山说着，在另外两台终端上快速操作起来。很快，他调出了另外两颗卫星WMAP和Planck的宇宙背景辐射实时数据，并将其变化显示为曲线——',\n",
       " '三条曲线在同步波动，一模一样。',\n",
       " '沙瑞山又搬出一台笔记本电脑，手忙脚乱地启动系统，插上宽带网线，然后打电话——汪淼听出他在联系乌鲁木齐射电观测基地——然后等待着。他没有对汪淼解释什么，两眼死盯着屏幕上的浏览器，汪淼能听到他急促的呼吸声。几分钟后，浏览器上出现了一个坐标窗口，一条红色曲线在窗口上出现，与另外三条进行着精确同步的波动。',\n",
       " '这样，三颗卫星和一套地面观测设备同时证实了一件事：宇宙在闪烁！',\n",
       " '“能将前面的曲线打印出来吗？”汪淼问。沙瑞山抹了一把头上的冷汗，点点头，移动鼠标启动了打印程序。汪淼迫不及待地抓过激光打印机吐出的第一张纸，用一支铅笔划过曲线，将波峰间的距离与他刚拿出来的那张莫尔斯电码表对照起来。',\n",
       " '短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短短长长、短短短长长、长长短短短，这是1108:21:37。',\n",
       " '短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短短长长、短短短长长、长短短短短，这是1108:21:36。',\n",
       " '短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短短长长、短短短长长、短短短短短，这是1108:21:35。',\n",
       " '……',\n",
       " '倒计时在宇宙尺度上继续，还剩1108小时。',\n",
       " '沙瑞山焦躁地来回踱步，不时在汪淼身后停下来看看他正在写出的那一串数字。“你真的不能把实情告诉我吗？！”他耐不住大声问。',\n",
       " '“沙博士，相信我，一时说不清的。”汪淼推开那一堆印着波动曲线的纸，盯着那行倒计时数字，“也许，三颗卫星和一个地面观测点都出现了故障。”',\n",
       " ...]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从本地文件系统中加载数据\n",
    "file = \"/mnt/user/linzhixin/mapreduce/data/三体.txt\"\n",
    "rdd = sc.textFile(file,3) #minPartitions=3，最少分成3份\n",
    "rdd.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把代码里的数据结构变成rdd。如果是数值迭代，官方推荐用range函数\n",
    "rdd = sc.parallelize(range(1,11),2) # numSlices=2 和minPartitions一样都是切分的。但是这个是固定数值，numPartitions是最少数值\n",
    "rdd.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 取rdd第一个数据、取前n个数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 其中，collect()函数用于把散布在集群中的被分片的数据汇总收集到driver上\n",
    "rdd = sc.parallelize(range(10),5) \n",
    "first_data = rdd.first()\n",
    "print(first_data)\n",
    "# 拿出前4个数据\n",
    "rdd.take(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一些action操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 在rdd里采样固定数量。\n",
    "实际上是融合了sample和take操作，然后帮你自动计算补齐了中间需要的值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 1, 8, 3, 9, 4, 2, 6, 7, 0]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#takeSample可以随机取若干个到Driver,第一个参数设置是否放回抽样\n",
    "rdd = sc.parallelize(range(10),5) \n",
    "sample_data = rdd.takeSample(False, num=10, seed=1024)\n",
    "sample_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 规约-reduce\n",
    "值得详细讲述。\n",
    "下面的代码首先把0～9分到5个区里，形成rdd，然后用reduce处理rdd。\n",
    "lambda运算有俩值，x是累积值，也是该区块上一次lambda的计算结果，y是当前这条数据。如果区块里是1,3,5，则一开始x=1,y=3，得4，x=4,y=5,得9.\n",
    "reduce首先在各个区内做lambda运算,此时各个区里得到一个数，它们排成一列也可以做lambda运算。所以reduce是两层计算。\n",
    "！：reduce是一种action，不是transfer操作，所以不产生新的rdd。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdd = sc.parallelize(range(10),5) \n",
    "rdd.reduce(lambda x,y:x+y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# foreach迭代对所有元素执行某个操作 / 使用累加器\n",
    "用spark sc注册一个累加器，并在lambda里使用它。\n",
    "foreach执行一个action，所以不设计创建新的rdd。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n"
     ]
    }
   ],
   "source": [
    "#foreach对每一个元素执行某种操作，不生成新的RDD\n",
    "#累加器用法详见共享变量\n",
    "rdd = sc.parallelize(range(10),5) \n",
    "accum = sc.accumulator(0) # 设定一个累加器，这样在transform操作时不会产生新的rdd。\n",
    "rdd.foreach(lambda x:accum.add(x))\n",
    "print(accum.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "3\n",
      "3\n",
      "7\n",
      "12\n",
      "6\n",
      "13\n",
      "21\n",
      "30\n"
     ]
    }
   ],
   "source": [
    "# 从输出可以看到，foreach迭代所有区块，执行pprint函数，输入只能是一个元素。同时，sum对每个区块是并行初始化的，不会有数据交互。\n",
    "sum = 0\n",
    "def pprint(x):\n",
    "    global sum \n",
    "    sum += x\n",
    "    print(sum)\n",
    "    \n",
    "rdd = sc.parallelize(range(10), 3)\n",
    "rdd.foreach(pprint)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 如果你的数据结构是list[tuple]，则可用countByKey数key的数量\n",
    "经测试，range(10)不是tuple，spark报错。tuple由三个元素组成，则仍然以第一个值为key。\n",
    "显然，如果这里的key是单词，value又全都是1，则可以统计单词数量了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int, {1: 1, '11': 1, 3: 1, 2: 1})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#countByKey对Pair RDD按key统计数量\n",
    "pairRdd = sc.parallelize([(1,1,2),(\"11\",4,3),(3,9,3),(2,16,3)]) \n",
    "pairRdd.countByKey()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 保存文件操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from glob import glob \n",
    "from fileinput import input # 这个类可以帮助流式地读取多个文件的line\n",
    "import tempfile\n",
    "\n",
    "# saveAsTextFile保存rdd为零散的文件到本地，每个前缀为part-的都是一个spark自己分数据分出的区块。\n",
    "# 如果我设置numSlices=2，part就有两个。\n",
    "# 可以直接看part文件。\n",
    "# ！！！saveAsTextFile必须是一个不存在的文件，防止被复写\n",
    "# \n",
    "save_file = \"/mnt/user/linzhixin/mapreduce/data/spark\"\n",
    "rdd = sc.parallelize(range(5),numSlices=2)\n",
    "with tempfile.TemporaryDirectory() as d1:\n",
    "    rdd.saveAsTextFile(save_file)\n",
    "    \n",
    "    print(''.join(sorted(input(glob(save_file + \"/part-0000*\")))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['./data/save/part-00001', './data/save/part-00000'] [] ['./data/save/part-00001']\n",
      "[] [] ['part-00001']\n",
      "['part-00001', 'part-00000'] [] ['part-00001']\n"
     ]
    }
   ],
   "source": [
    "from glob import glob, glob0, glob1\n",
    "save_file = \"./data/spark\"\n",
    "print(glob(save_file + \"/part-0000*\"), glob(save_file + \"/part-0000\"), glob(save_file + \"/part-00001\"))\n",
    "print(glob0(save_file, \"part-0000*\"), glob0(save_file, \"part-0000\"), glob0(save_file, \"part-00001\"))\n",
    "print(glob1(save_file, \"part-0000*\"), glob1(save_file, \"part-0000\"), glob1(save_file, \"part-00001\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一些transformation操作\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# map\n",
    "重点是内部操作过程。已知transformation创建新rdd，lazy excutor导致action时才开始运行 stage DAG，所以下面的代码背后的流程如下：\n",
    "1. driver 根据map的lambda函数把DAG拆成stage程度，告诉cluster manager创建新rdd，即给计算留位置。\n",
    "2. collect是action，在action时，driver把stage task给work node，work node执行操作，并把内容存在新rdd上。\n",
    "试想，如果map时就开始执行，那么如果我只写rdd.map，而不是rdd=rdd.map，那么work node会计算结果给新rdd，计算完，cluster manager会直接释放掉新rdd，因为没有任何handler使用这个新rdd。考虑到数据量极大，一空转就资源浪费了。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "def complex_task(x):\n",
    "    while x != 1:\n",
    "        if x % 2 == 1:\n",
    "            x = x + 1\n",
    "        else:\n",
    "            x = x // 2\n",
    "    return x\n",
    "\n",
    "print(complex_task(92374914))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_time: 0.7297718524932861\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "a = [i for i in range(1,1000000)]\n",
    "random.shuffle(a)\n",
    "rdd = sc.parallelize(a,8)\n",
    "start_time = time.time()\n",
    "rdd = rdd.map(complex_task)\n",
    "rdd.collect()\n",
    "end_time = time.time()\n",
    "print('total_time:', end_time-start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_time: 1.8763177394866943\n"
     ]
    }
   ],
   "source": [
    "# 我们测试map计算1000个值的速度，上面是spark方法，下面是普通方法\n",
    "start_time = time.time()\n",
    "for i in range(1,1000000):\n",
    "    complex_task(i)\n",
    "end_time = time.time()\n",
    "print('total_time:', end_time-start_time)\n",
    "\n",
    "# 在数量达到一千万时，spark速度高于单机。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 过滤\n",
    "数值过滤"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['两年以后，大兴安岭。',\n",
       " '《寂静的春天》封面 ',\n",
       " '“可这是你的笔迹。”',\n",
       " '“所以只能这么办。”',\n",
       " '6.\\u3000射手和农场主 ',\n",
       " '医生写处方的笔停了。',\n",
       " '“你看到一串数字？”',\n",
       " '“当然，三天以后。”',\n",
       " '“那倒计时将继续。”',\n",
       " '“那怎么计时呢？” ',\n",
       " '“什么是乱纪元？” ',\n",
       " '“以前看到过的。” ',\n",
       " '“你知道这不可能！”',\n",
       " '“这东西现在哪儿？”',\n",
       " '“我夜里从不看天。”',\n",
       " '“邪乎到家必有鬼。”',\n",
       " '“结果可想而知。” ',\n",
       " '“什么？熄灭？！” ',\n",
       " '“球里面还有人？” ',\n",
       " '“脱水！脱水！！” ',\n",
       " '1.物理学：【略】 ',\n",
       " '2.生物学：【略】 ',\n",
       " '五、相关政策与战略 ',\n",
       " '“你应该能想到的。”',\n",
       " '“每个人都见过。” ',\n",
       " '“其他有关情况呢？”',\n",
       " '“有，肯定有油味！”',\n",
       " '“你尽量按原话说。”',\n",
       " '“成计算机队列！” ',\n",
       " '汪淼一阵激动和紧张。',\n",
       " '“我们，是同志了。”',\n",
       " '20.\\u3000三体、远征 ',\n",
       " '“要布置狙击手吗？”',\n",
       " '“当心！放射性！！”',\n",
       " '审问者：出生日期？ ',\n",
       " '“我是说从精神上。”',\n",
       " '“你们不该忏悔吗？”',\n",
       " '“他给你们看病吗？”',\n",
       " '叶文洁平静地点点头。',\n",
       " '叶文洁：几乎是的。 ',\n",
       " '叶文洁：您很聪明。 ',\n",
       " '“现有的数量够吗？”',\n",
       " '“目标距琴三公里！”',\n",
       " '“目标距琴两公里！”',\n",
       " '“目标距琴一公里！”',\n",
       " '“如何产生神迹呢？”',\n",
       " '将维度收缩至三维。 ',\n",
       " '将维度收缩至四维。 ',\n",
       " '将维度收缩至六维。 ',\n",
       " '太空中的小球消失了。',\n",
       " '35.\\u3000尾声·遗址 ',\n",
       " '1989.03.21',\n",
       " '地球往事·黑暗森林 ',\n",
       " '“你是……小罗吧？”',\n",
       " '“哦，都过去了……”',\n",
       " '“为什么这么说呢？”',\n",
       " '伊文斯：“同义词？”',\n",
       " '伊文斯：“为什么？”',\n",
       " '字幕：我害怕你们。 ',\n",
       " 'NASA Logo ',\n",
       " '字幕：需要证实吗？ ',\n",
       " '“就是逃亡基金啊。”',\n",
       " '“是是，这我知道。”',\n",
       " '“没有，很老的吧？”',\n",
       " '“要多想。”父亲说。',\n",
       " '“以你为主人公吗？”',\n",
       " '“为什么不是春天？”',\n",
       " '“是，很早的事了。”',\n",
       " '“这有什么疑问吗？”',\n",
       " '“时间卡得这么紧？”',\n",
       " '萨伊的手指向了他——',\n",
       " '“我只是个普通人。”',\n",
       " '“那个……坎特呢？”',\n",
       " '“向全世界发布了。”',\n",
       " '“当然也在新闻里。”',\n",
       " '伽尔宁严肃地点点头。',\n",
       " '“先说你的事儿吧。”',\n",
       " '“那你要他做什么？”',\n",
       " '“不客气，您说吧。”',\n",
       " '“罗老师好！”她说。',\n",
       " '天啊，罗辑在心里说。',\n",
       " '“晚霞的眼睛是吗？”',\n",
       " '“最后？你在哪儿？”',\n",
       " '“外星人不是人吗？”',\n",
       " '“肯定行，你说吧。”',\n",
       " '人真是个奇怪的东西。',\n",
       " '“保存到什么时候？”',\n",
       " '“您认为这可能么？”',\n",
       " '泰勒仍然说不出话来。',\n",
       " '“将军，可能不行。”',\n",
       " '“不能多拍几张吗？”',\n",
       " '“我知道那种感觉。”',\n",
       " '“从中央美院毕业？”',\n",
       " '“现在不都这样吗？”',\n",
       " '“请说说这个办法。”',\n",
       " '“距太阳最近的是？”',\n",
       " '“是的。”罗辑回答。',\n",
       " '“会有什么作用呢？”',\n",
       " '“定量都是多少啊？”',\n",
       " '这话引起了一阵共鸣。',\n",
       " '“当然是三体世界。”',\n",
       " '“特此提交本提案。”',\n",
       " '“当然，我们报案。”',\n",
       " '“是因为经济转型？”',\n",
       " '“后来呢？”大史问。',\n",
       " '“是的，简单多了。”',\n",
       " '“不过你学得很快。”',\n",
       " '“一个浪漫的军种。”',\n",
       " '“可我们不是敌人。”',\n",
       " '“是的，深海状态。”',\n",
       " '“东方，不要减速。”',\n",
       " '“再把倍数调大些！”',\n",
       " '赵鑫： 什么速度？ ',\n",
       " '那就是永远到不了。 ',\n",
       " '黑，真他妈的黑啊。 ',\n",
       " '“我和你一起去吧。”',\n",
       " '“不用，我自己去。”',\n",
       " '“为什么？”市长问。',\n",
       " '对此罗辑早就知道了。',\n",
       " '水滴是来封死太阳的。',\n",
       " '“你可是个执法者。”',\n",
       " '“我选择与你交流？”',\n",
       " '已经按你说的做了。 ',\n",
       " '已经按你说的做了。 ',\n",
       " '地球往事·死神永生 ',\n",
       " '“当然，这你放心。”',\n",
       " '“你相信有上帝吗？”',\n",
       " '“可是……海洋呢？”',\n",
       " '大自然真是自然的吗？',\n",
       " '“别说是我打听的。”',\n",
       " '他感觉自己已经死了。',\n",
       " '“站起来。”维德说。',\n",
       " '“只送大脑。”他说。',\n",
       " '这游戏真有趣，是吧？',\n",
       " '“那，他吃什么？！”',\n",
       " '“她说她还等着我。”',\n",
       " '法官：你没有这么做。',\n",
       " '法官：遗体去了哪里？',\n",
       " '法官：他们的反应呢？',\n",
       " '然后程心看到了罗辑。',\n",
       " '但也可能就在下一秒。',\n",
       " '好在解脱就要到来了。',\n",
       " '水滴没有攻击执剑人。',\n",
       " '三体舰队达到了光速！',\n",
       " '所有的长矛都已折断。',\n",
       " '“你是程心博士吗？”',\n",
       " '“艾AA和弗雷斯。”',\n",
       " '但已经有人在那里了。',\n",
       " '“‘万有引力’号？”',\n",
       " '这是一艘宇宙飞船吗？',\n",
       " '所有的鱼都在这里吗？',\n",
       " '把海弄干的鱼不在。 ',\n",
       " '对不起，这话很费解。',\n",
       " '能给我们一些建议吗？',\n",
       " 'M1金牛座蟹状星云 ',\n",
       " '“是在为自己种吗？”',\n",
       " '绿灯熄灭，黄灯亮起。',\n",
       " '“是，今年年景好。”',\n",
       " '分别的时刻即将来临。',\n",
       " '直到王子和公主长大。',\n",
       " '他们谈起了深水王子。',\n",
       " '“人们说他是巨人。”',\n",
       " '“墓岛呢？”宽姨问。',\n",
       " '“现在什么时间了？”',\n",
       " '在火光中晶莹地闪亮。',\n",
       " '于是他们决定冒险了。',\n",
       " '“宽姨呢？”长帆问。',\n",
       " '“这次我们乘帆船。”',\n",
       " '2.远距离躲避计划：',\n",
       " '采集光能，降低光速。',\n",
       " '“我们会赔你一艘。”',\n",
       " '这就是宇宙安全声明。',\n",
       " '“我知道你不喜欢。”',\n",
       " '“我喜欢光速飞船。”',\n",
       " '“不对，好好想想。”',\n",
       " '“因为下面有人群。”',\n",
       " '“我已经遇到过了。”',\n",
       " '“你到底想说什么？”',\n",
       " '“木卫二？”程心问。',\n",
       " '希望找着他们所要的。',\n",
       " '那是一小块凝固的时间',\n",
       " '藏好自己，做好清理。',\n",
       " '很有意思，很有意思！',\n",
       " '很有意思，很有意思。',\n",
       " '藏好自己，做好清理。',\n",
       " '我们眼中的星星像幽灵',\n",
       " '也许这真的是一封信？',\n",
       " '“老师，钱的话……”',\n",
       " '“你发现什么了吗？”',\n",
       " '“没有，只是直觉。”',\n",
       " '“逃逸速度是多少？”',\n",
       " '“还能有多少时间？”',\n",
       " '“这幅给我留下吧。”',\n",
       " '“一共有几个世界？”',\n",
       " '所有的鱼都在这里吗？',\n",
       " '把海弄干的鱼不在。 ',\n",
       " '对不起，这话很费解。',\n",
       " '“天啊，你是说……”',\n",
       " '“见过，见得不多。”',\n",
       " '“目光随着它移动。”',\n",
       " '“把字刻在石头上。”',\n",
       " '“我真的不明白……”',\n",
       " '我们度过了幸福的一生',\n",
       " '“你知道这是什么？”',\n",
       " '“那，这好像是……”',\n",
       " '这让人想到暗物质。 ',\n",
       " '喜悦他们所见到的； ',\n",
       " '地球往事系列时间线 ',\n",
       " '1986年\\u3000程心出生',\n",
       " '6. 射手和农场主 ',\n",
       " '20. 三体、远征 ',\n",
       " '35. 尾声·遗址 ',\n",
       " '序·心事浩渺连广宇 ',\n",
       " '广播纪元7年，程心 ',\n",
       " '广播纪元7年，智子 ',\n",
       " '地球往事系列时间线 ']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "rdd = sc.textFile('/mnt/user/linzhixin/mapreduce/data/三体.txt',3)\n",
    "rdd.filter(lambda x:len(x)==10).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 采样,第一个参数设置每个元素是无放回/有放回采样，第二个设置每个元素采样的概率/每个元素采样数量\n",
    "具体来说：\n",
    "False时，每个元素都以fraction为概率进行二项分布采样，因此返回数量不固定，使用的是getUniformSample函数\n",
    "True时，每个元素依照franction设置一个初值exp(fraction)，然后设p=1，每次都在p上累乘一个0～1的随机数，直到p<=exp(fraction)，此时累乘的次数-1就是要采样的下标。所以返回数量也不一定，而且显然容易采样到前面的。\n",
    "\n",
    "与takesample不一样，这个是transformation，takesample是action，因为它融合了sample和take，还额外指定了采样数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 2, 2, 3, 4, 5, 6, 6, 7, 9, 9]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdd = sc.parallelize(range(10), 1)#('/mnt/user/linzhixin/mapreduce/data/三体.txt', 3)\n",
    "rdd.sample(True,1).collect()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 去重\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 1, 5, 2, 3]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdd = sc.parallelize([1,1,2,2,3,3,4,5])\n",
    "rdd.distinct().collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 大杂烩，不一一列举了，看注释"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subtract rdd数据集减法\n",
    "#subtract找到属于前一个rdd而不属于后一个rdd的元素\n",
    "a = sc.parallelize(range(10))\n",
    "b = sc.parallelize(range(5,15))\n",
    "a.subtract(b).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 3, 4, 5, 6, 7]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# union 数据集合并\n",
    "a = sc.parallelize(range(5))\n",
    "b = sc.parallelize(range(3,8))\n",
    "a.union(b).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intersection 交集\n",
    "a = sc.parallelize(range(1,6))\n",
    "b = sc.parallelize(range(3,9))\n",
    "a.intersection(b).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('LiLei', 'HanMeiMei'),\n",
       " ('LiLei', 'Lily'),\n",
       " ('Tom', 'HanMeiMei'),\n",
       " ('Tom', 'Lily')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 高端的，笛卡尔积，即两个rdd的元素配对\n",
    "#cartesian笛卡尔积\n",
    "boys = sc.parallelize([\"LiLei\",\"Tom\"])\n",
    "girls = sc.parallelize([\"HanMeiMei\",\"Lily\"])\n",
    "boys.cartesian(girls).collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(3, 2, 2), (4, 1, 1), (1, 2, 3)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sortby排序，按照返回值排序\n",
    "def sort_func(x):\n",
    "    return (x[0]-3)**2\n",
    "    \n",
    "rdd = sc.parallelize([(1,2,3),(3,2,2),(4,1,1)])\n",
    "rdd.sortBy(sort_func).collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# zip 打包，和python的zip()一样，所以两个rdd 元素数量必须一样\n",
    "\n",
    "rdd_name = sc.parallelize([\"LiLei\",\"Hanmeimei\",\"Lily\"])\n",
    "rdd_age = sc.parallelize([19,18,20])\n",
    "\n",
    "rdd_zip = rdd_name.zip(rdd_age)\n",
    "print(rdd_zip.collect())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('LiLei', 0), ('Hanmeimei', 1), ('Lily', 2), ('Lucy', 3), ('Ann', 4), ('Dachui', 5), ('RuHua', 6)]\n"
     ]
    }
   ],
   "source": [
    "# 只是想给每个元素加个编号，用zipWithIndex\n",
    "# 好好好，现在知道排序了，怎么之前foreach的时候就没有排序呢\n",
    "rdd_name =  sc.parallelize([\"LiLei\",\"Hanmeimei\",\"Lily\",\"Lucy\",\"Ann\",\"Dachui\",\"RuHua\"],4)\n",
    "rdd_index = rdd_name.zipWithIndex()\n",
    "print(rdd_index.collect())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一堆基于key value的算子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2 5\n",
      "1 3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('hello', 4), ('world', 7)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reduceByKey对相同的key对应的values应用二元归并操作。\n",
    "#显然，reduceByKey用key去map各个值，然后reduce每个块\n",
    "def reducebykey(x,y):\n",
    "    print(x,y)\n",
    "    return x+y\n",
    "    \n",
    "\n",
    "rdd = sc.parallelize([(\"hello\",1),(\"world\",2),\n",
    "                               (\"hello\",3),(\"world\",5)])\n",
    "rdd.reduceByKey(reducebykey).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('hello', <pyspark.resultiterable.ResultIterable at 0x7fbc64b33b80>),\n",
       " ('world', <pyspark.resultiterable.ResultIterable at 0x7fbc641440d0>)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#groupByKey将相同的key对应的values收集成一个Iterator\n",
    "rdd = sc.parallelize([(\"hello\",1),(\"world\",2),(\"hello\",3),(\"world\",5)])\n",
    "rdd.groupByKey().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('world', 2), ('hello', 1), ('China', 3), ('Beijing', 5)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sortByKey按照key排序,可以指定是否降序\n",
    "rdd = sc.parallelize([(\"hello\",1),(\"world\",2),\n",
    "                               (\"China\",3),(\"Beijing\",5)])\n",
    "rdd.sortByKey(False).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('HanMeiMei', (16, 'female')), ('LiLei', (18, 'male'))]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#join相当于根据key进行内连接\n",
    "age = sc.parallelize([(\"LiLei\",18),\n",
    "                        (\"HanMeiMei\",16),(\"Jim\",20)])\n",
    "gender = sc.parallelize([(\"LiLei\",\"male\"),\n",
    "                        (\"HanMeiMei\",\"female\"),(\"Lucy\",\"female\")])\n",
    "age.join(gender).collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('HanMeiMei', (16, None)), ('LiLei', (18, 'male'))]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#leftOuterJoin相当于关系表的左连接\n",
    "\n",
    "age = sc.parallelize([(\"LiLei\",18),\n",
    "                        (\"HanMeiMei\",16)])\n",
    "gender = sc.parallelize([(\"LiLei\",\"male\"),(\"Lucy\",\"female\")])\n",
    "age.leftOuterJoin(gender).collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('HanMeiMei', (None, 'female')), ('LiLei', (18, 'male'))]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rightOuterJoin相当于关系表的右连接\n",
    "age = sc.parallelize([(\"LiLei\",18),(\"Jim\",20)])\n",
    "gender = sc.parallelize([(\"LiLei\",\"male\"),\n",
    "                        (\"HanMeiMei\",\"female\")])\n",
    "age.rightOuterJoin(gender).collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('b', (<pyspark.resultiterable.ResultIterable object at 0x7fb270b0f190>, <pyspark.resultiterable.ResultIterable object at 0x7fb270122980>)), ('a', (<pyspark.resultiterable.ResultIterable object at 0x7fb270122230>, <pyspark.resultiterable.ResultIterable object at 0x7fb2701221a0>))]\n",
      "[2]\n"
     ]
    }
   ],
   "source": [
    "#cogroup相当于对两个输入分别groupByKey然后再对结果进行groupByKey\n",
    "# 是把两个rdd groupByKey的方式\n",
    "\n",
    "x = sc.parallelize([(\"a\",1),(\"b\",2),(\"a\",3)])\n",
    "y = sc.parallelize([(\"a\",2),(\"b\",3),(\"b\",5)])\n",
    "\n",
    "result = x.cogroup(y).collect()\n",
    "print(result)\n",
    "print(list(result[0][1][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('b', 2), ('c', 3)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subtractByKey去除x中那些key也在y中的元素\n",
    "\n",
    "x = sc.parallelize([(\"a\",1),(\"b\",2),(\"c\",3)])\n",
    "y = sc.parallelize([(\"a\",2),(\"d\",(1,2))])\n",
    "\n",
    "x.subtractByKey(y).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10 1\n",
      "10 2\n",
      "10 3\n",
      "10 5\n",
      "11 13\n",
      "12 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('world', 27), ('hello', 24)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#foldByKey\n",
    "# 给所有值添加一个初值，在此之上再做reduceByKey\n",
    "\n",
    "def _foldByKey(x,y):\n",
    "    print(x,y)\n",
    "    return x+y\n",
    "    \n",
    "\n",
    "rdd = sc.parallelize([(\"hello\",1),(\"world\",2),\n",
    "                               (\"hello\",3),(\"world\",5)],2)\n",
    "rdd.foldByKey(10, _foldByKey).collect()\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 缓存操作 \n",
    "1. rdd.cache() == persist(StorageLevel.MEMORY_ONLY)\n",
    "spark和hadoop的区别之一就是，前者可以在缓存操作，后者对磁盘操作。\n",
    "\n",
    "spark怎么对缓存操作？即你对一个存在磁盘的rdd操作多步时，可以让它缓存到缓存中。\n",
    "\n",
    "对缓存的操作不会切断它和原始磁盘数据的关系，只有自己选择存到磁盘中，才会多出一份当前的rdd。否则计算图完成后，存回原始rdd磁盘位置。\n",
    "\n",
    "有缓存，就有缓存卸载。可以把卸载看作action，即立即执行，而不是等到下一个action时才开始卸载。\n",
    "\n",
    "2. persist 现在版本默认memory_only\n",
    "还提供其他模式。![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.001 b_time: 0.27327871322631836\n",
      "2.001 c_time: 0.4830811023712158\n"
     ]
    }
   ],
   "source": [
    "#cache缓存到内存中，使用存储级别 MEMORY_ONLY。\n",
    "#MEMORY_ONLY意味着如果内存存储不下，放弃存储其余部分，需要时重新计算。\n",
    "#\n",
    "def complex_task(x,y):\n",
    "    while x != 1:\n",
    "        if x % 2 == 1:\n",
    "            x += 1\n",
    "        else:\n",
    "            x = x // 2\n",
    "    return  x+y\n",
    "        \n",
    "b = sc.parallelize(range(1000,2000),5)\n",
    "c = sc.parallelize(range(1000,2000),5)\n",
    "c.cache()\n",
    "\n",
    "st = time.time()\n",
    "sum_a = b.reduce(complex_task)\n",
    "cnt_a = b.count()\n",
    "mean_a = sum_a/cnt_a\n",
    "print(mean_a, 'b_time:', time.time()-st)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 0:>                                                          (0 + 5) / 5]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4999.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# persist设置MEMORY_AND_DISK会尽可能把分区加载到cache里。\n",
    "# unpersist释放缓存，是真的释放，不写回，用于去掉中间暂存信息，给其他rdd腾cache位置。\n",
    "# 写回要用saveAsTextFile、setCheckpointDir或者其他save方法。\n",
    "from  pyspark.storagelevel import StorageLevel\n",
    "a = sc.parallelize(range(10000),5)\n",
    "a.persist(StorageLevel.MEMORY_AND_DISK)\n",
    "sum_a = a.reduce(lambda x,y:x+y)\n",
    "cnt_a = a.count()\n",
    "mean_a = sum_a/cnt_a\n",
    "\n",
    "a.unpersist() #立即释放缓存\n",
    "print(mean_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('LiLei', 0), ('Hanmeimei', 1), ('LiLy', 2)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checkpoint 将数据设置成检查点，写入到磁盘中。\n",
    "# 如果我计算出两个rdd，想写到两个dir里，就比较恶心了，得先设一个dir，存完，再设一个dir。\n",
    "# 写入操作是唯一永久保存的\n",
    "sc.setCheckpointDir(\"/mnt/user/linzhixin/mapreduce/data/spark\")\n",
    "rdd_students = sc.parallelize([\"LiLei\",\"Hanmeimei\",\"LiLy\",\"Ann\"],2)\n",
    "\n",
    "rdd_students_idx = rdd_students.zipWithIndex() \n",
    "\n",
    "#设置检查点后，可以避免重复计算，不会因为zipWithIndex重复计算触发不一致的问题\n",
    "rdd_students_idx.checkpoint() \n",
    "rdd_students_idx.take(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 共享变量 \n",
    "之前map的时候做累加，就看到了global变量会在每个分区做复制，然后隔离地计算。\n",
    "现在有共享变量可以解决这个问题。\n",
    "spark提供广播和累加器两种共享变量。\n",
    "\n",
    "1. 广播，把一个数据复制到所有分区。\n",
    "2. 累加器，用sc.accumulator(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#广播变量 broadcast 不可变，在所有节点可读\n",
    "\n",
    "broads = sc.broadcast(100)\n",
    "\n",
    "rdd = sc.parallelize(range(10))\n",
    "print(rdd.map(lambda x:x+broads.value).collect())\n",
    "\n",
    "print(broads.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#累加器 只能在Driver上可读，在其它节点不可见，只能进行累加操作。\n",
    "#  sc.accumulator(0)的0是累加器初始值。\n",
    "\n",
    "total = sc.accumulator(30)\n",
    "rdd = sc.parallelize(range(10),3)\n",
    "\n",
    "rdd.foreach(lambda x:total.add(x))\n",
    "total.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5999999999999996"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 例子：\n",
    "# 计算数据的平均值\n",
    "rdd = sc.parallelize([1.1,2.1,3.1,4.1])\n",
    "total = sc.accumulator(0)\n",
    "count = sc.accumulator(0)\n",
    "\n",
    "def func(x):\n",
    "    total.add(x)\n",
    "    count.add(1)\n",
    "    \n",
    "rdd.foreach(func)\n",
    "\n",
    "total.value/count.value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分区操作\n",
    "glom：将一个分区内的数据转换为一个列表作为一行。\n",
    "\n",
    "coalesce：shuffle可选，默认为False情况下窄依赖，不能增加分区。repartition和partitionBy调用它实现。\n",
    "\n",
    "repartition：按随机数进行shuffle，相同key不一定在同一个分区\n",
    "\n",
    "partitionBy：按key进行shuffle，相同key放入同一个分区\n",
    "\n",
    "HashPartitioner：默认分区器，根据key的hash值进行分区，相同的key进入同一分区，效率较高，key不可为Array.\n",
    "\n",
    "RangePartitioner：只在排序相关函数中使用，除相同的key进入同一分区，相邻的key也会进入同一分区，key必须可排序。\n",
    "\n",
    "TaskContext: 获取当前分区id方法 TaskContext.get.partitionId\n",
    "\n",
    "mapPartitions：每次处理分区内的一批数据，适合需要分批处理数据的情况，比如将数据插入某个表，每批数据只需要开启一次数据库连接，大大减少了连接开支\n",
    "\n",
    "mapPartitionsWithIndex：类似mapPartitions，提供了分区索引，输入参数为（i，Iterator）\n",
    "\n",
    "foreachPartition：类似foreach，但每次提供一个Partition的一批数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1, 2], [3, 4], 10], [[5, 6], [7, 8], 10]]\n",
      "[1, 2, 3, 4, 10, 5, 6, 7, 8, 10]\n"
     ]
    }
   ],
   "source": [
    "#glom将一个分区内的数据转换为一个列表作为一行，所以每个分区也就只有一个元素了。\n",
    "# 它的逆操作是flatMap，把分区里的list打散成elem\n",
    "\n",
    "def enhance(x):\n",
    "    # print(x)\n",
    "    return x + [10]\n",
    "\n",
    "\n",
    "\n",
    "def flat(x):\n",
    "    ret = []\n",
    "    for item in x:\n",
    "        if isinstance(item, list):\n",
    "            item = flat(item)\n",
    "            ret.extend(item)\n",
    "        else:\n",
    "            ret.append(item)\n",
    "    return ret\n",
    "    \n",
    "    \n",
    "a = sc.parallelize([[1,2],[3,4],[5,6],[7,8]],2)\n",
    "b = a.glom()\n",
    "b = b.map(enhance)\n",
    "print(b.collect())\n",
    "b = b.flatMap(flat)\n",
    "print(b.collect())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "[[], [0], [1], [2], [3], [4], [5], [6], [7], [8]]\n",
      "[[2, 5, 7, 8], [0], [1, 3, 4, 6]]\n"
     ]
    }
   ],
   "source": [
    "#coalesce 默认shuffle为False，不能增加分区，只能减少分区\n",
    "#如果要增加分区，要设置shuffle = true\n",
    "#parallelize等许多操作可以指定分区数\n",
    "\n",
    "# 如果shuffle=False，则每次合并都合并最小的两个分区。\n",
    "# 如果shuffle=True，则每次随机合并两个分区，但是看起来也不那么随机。\n",
    "a = sc.parallelize(range(9),10)  \n",
    "print(a.getNumPartitions())\n",
    "print(a.glom().collect())\n",
    "b = a.coalesce(3,shuffle=True) \n",
    "print(b.glom().collect())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6, 7, 8, 9], [0, 1, 2, 3, 4, 5], [], []]\n",
      "[[('a', 1), ('a', 2), ('c', 3)], [('a', 1)]]\n"
     ]
    }
   ],
   "source": [
    "#repartition = self.coalesce(numPartitions, shuffle=True)\n",
    "a = sc.parallelize(range(10),3)  \n",
    "c = a.repartition(4) \n",
    "print(c.glom().collect())\n",
    "#repartition按随机数进行shuffle，相同key不一定在一个分区\n",
    "a = sc.parallelize([(\"a\",1),(\"a\",1),(\"a\",2),(\"c\",3)])  \n",
    "c = a.repartition(2)\n",
    "print(c.glom().collect())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[('c', 3)], [('a', 1), ('a', 1), ('a', 2)]]\n"
     ]
    }
   ],
   "source": [
    "#partitionBy按key进行shuffle，相同key一定在一个分区\n",
    "a = sc.parallelize([(\"a\",1),(\"a\",1),(\"a\",2),(\"c\",3)],2)  \n",
    "c = a.partitionBy(2)\n",
    "print(c.glom().collect())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mapPartitions可以对每个分区分别执行操作\n",
    "#每次处理分区内的一批数据，适合需要按批处理数据的情况\n",
    "#例如将数据写入数据库时，可以极大的减少连接次数。\n",
    "#mapPartitions的输入分区内数据组成的Iterator，其输出也需要是一个Iterator\n",
    "#以下例子查看每个分区内的数据,相当于用mapPartitions实现了glom的功能。\n",
    "\n",
    "# 就是一次性提供每个分区的所有元素，然后以分区为单位做数据处理。\n",
    "a = sc.parallelize(range(10),2)\n",
    "a.mapPartitions(lambda it:iter([list(it)])).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0|10', '1|45']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(0, 0), (1, 1), (1, 2), (2, 3), (2, 4)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mapPartitionsWithIndex可以获取两个参数\n",
    "#即分区id和每个分区内的数据组成的Iterator\n",
    "\n",
    "# 实际做的事：glom分区，collect每个list，为它们设定key数字编号，再执行func\n",
    "a = sc.parallelize(range(11),2)\n",
    "\n",
    "def func(pid,it):\n",
    "    s = sum(it)\n",
    "    return(iter([str(pid) + \"|\" + str(s)]))\n",
    "    \n",
    "b = a.mapPartitionsWithIndex(func)\n",
    "print(b.collect())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0), (1, 1), (1, 2), (2, 3), (2, 4)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#利用TaskContext可以获取当前每个元素的分区\n",
    "from pyspark.taskcontext import TaskContext\n",
    "a = sc.parallelize(range(5),3)\n",
    "c = a.map(lambda x:(TaskContext.get().partitionId(),x))\n",
    "c.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100\n",
      "66\n",
      "33\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "199.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#foreachPartition对每个分区分别执行操作\n",
    "#范例：求每个分区内最大值的和\n",
    "\n",
    "# 实际就是先mapPartition再func，再用.count()激活计算\n",
    "total = sc.accumulator(0.0)\n",
    "\n",
    "a = sc.parallelize(range(1,101),3)\n",
    "\n",
    "def func(it):\n",
    "    print(max(it))\n",
    "    total.add(max(it))\n",
    "    \n",
    "a.foreachPartition(func)\n",
    "total.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210, 20, (((0, 6), 7), 7))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#aggregate是一个Action操作\n",
    "#aggregate比较复杂，先对每个分区执行一个函数，再对每个分区结果执行一个合并函数。\n",
    "#例子：求元素之和以及元素个数\n",
    "#三个参数，第一个参数为初始值，第二个为分区执行函数，第三个为结果合并执行函数。\n",
    "\n",
    "# 与reduce不同，它和累加器一样，但是自动创建。自动创建一个（0，0）数据结构，与每个分区内元素做计算，之后在分区间做reduce\n",
    "rdd = sc.parallelize(range(1,21),3)\n",
    "def inner_func(t,x):\n",
    "    return((t[0]+x,t[1]+1,t[2]+1))\n",
    "\n",
    "def outer_func(p,q):\n",
    "    return((p[0]+q[0],p[1]+q[1],(p[2],q[2])))\n",
    "\n",
    "rdd.aggregate((0,0,0),inner_func,outer_func)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('b', 3), ('a', 2), ('c', 2)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#aggregateByKey的操作和aggregate类似，但是会对每个key分别进行操作\n",
    "#第一个参数为初始值，第二个参数为分区内归并函数，第三个参数为分区间归并函数\n",
    "\n",
    "a = sc.parallelize([(\"a\",1),(\"b\",1),(\"c\",2),\n",
    "                             (\"a\",2),(\"b\",3)],3)\n",
    "b = a.aggregateByKey(0,lambda x,y:max(x,y),\n",
    "                            lambda x,y:max(x,y))\n",
    "b.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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